BioMedInformatics doi: 10.3390/biomedinformatics6030026

Authors: Shamsudeen Abdullahi Yuttapong Jiraraksopakun Apichai Bhatranand Anita Manassakorn Sunee Chansangpetch Kitiya Ratanawongphaibul Visanee Tantisevi

Glaucoma is a chronic progressive optic neuropathy causing irreversible blindness globally, underscoring the need for reliable diagnostic tools. While visual field (VF) testing remains the clinical standard, it has significant limitations, including subjective variability and patient cooperation difficulties. Optical coherence tomography (OCT) offers objective structural assessment. Recent deep learning approaches for VF prediction from OCT data can achieve high accuracy, but require raw three-dimensional volumetric data and substantial computational infrastructure that limit their deployment in routine clinical practice. We developed a lightweight convolutional neural network that predicts VF sensitivity from standard two-dimensional OCT thickness maps routinely available in clinical settings. The architecture integrates multiscale depthwise separable convolutions with attention mechanisms and employs an Exponentially Weighted Mean Squared Error loss function to enhance accuracy in clinically critical low-sensitivity regions. Using data from 241 subjects with five-fold cross-validation, our model achieved mean absolute error of 3.32 ± 2.35 dB and correlation of 0.74. This approach addresses the practical deployment limitations of existing methods while maintaining competitive accuracy, enabling implementation in resource-constrained clinical settings for patients who cannot reliably perform standard perimetry.

BioMedInformatics doi: 10.3390/biomedinformatics6030027

Authors: Diana G. Kiseleva Maria A. Kazakova Tatiana Yu. Plyusnina Yuliya V. Markina Alexander M. Markin

Myocardial edema is associated with cardiac electrical instability, but the cellular mechanisms linking osmotic cell swelling to arrhythmias remain unclear. Hypoosmotic conditions are hypothesized to drive transitions between dynamical regimes (e.g., spiral waves and multiple wavelets), producing distinct calcium oscillatory dynamics that act as markers of the underlying electrophysiological state. This study presents an integrated computational framework combining analysis of optical mapping data with mechanistic mathematical modeling to investigate calcium dynamics in cardiomyocyte monolayers under varying extracellular osmolality conditions. We developed an enhanced signal processing pipeline that reconstructs dynamic baselines from local minima using piecewise linear interpolation, enabling robust detection and characterization of calcium transients in highly heterogeneous and aperiodic signals. The computational workflow incorporated peak detection algorithms adapted for irregular oscillatory patterns, extraction of calcium transient features (amplitude, time to peak, decay durations at 30%, 50%, and 80% of peak amplitude) across spatial regions corresponding to different excitation regimes, and mathematical modeling to investigate the effects of hypoosmotic swelling at a cellular level. The parameters of the Gattoni (2016) rat ventricular cardiomyocyte model were modified to match experimental observations of the calcium transients. Simulation suggests that hypoosmotic swelling increases sarcolemmal calcium pump activity and elevates cytosolic concentrations of calmodulin and troponin, promoting alternans and delayed afterdepolarizations.

BioMedInformatics doi: 10.3390/biomedinformatics6030025

Authors: Alpamis Kutlimuratov Dilshod Eshmurodov Fotima Tulaganova Akhmet Utegenov Piratdin Allayarov Jamshid Khamzaev Islambek Saymanov Fazliddin Makhmudov

Background: Accurate and early detection of hematological disorders from microscopic peripheral blood smear images remains a technically challenging task due to inherent imaging limitations, including noise contamination, low contrast, staining variability, and significant cellular overlap. Conventional deep learning-based object detection frameworks often exhibit limited robustness under such conditions and demonstrate reduced sensitivity to small-scale morphological structures, particularly platelets and abnormal cell variants. Methods: To address these challenges, this study proposes a hybrid detection framework that integrates a fuzzy logic-driven image preprocessing module with the YOLOv11 object detection architecture. The proposed preprocessing pipeline employs adaptive fuzzy membership functions to normalize pixel intensity distributions, suppress high-frequency noise, and enhance edge-defined cellular boundaries. This transformation produces a structurally optimized feature representation, improving downstream feature extraction and localization performance. The proposed framework was evaluated on a curated dataset of 3000 annotated microscopic blood smear images spanning five hematological classes. Results: Experimental results show that the fuzzy logic module improves mAP@0.5 by +3.4% and mAP@0.5:0.95 by +3.6%, confirming its effectiveness in enhancing both classification and localization accuracy. Conclusions: These findings demonstrate the robustness and practical applicability of the proposed hybrid approach under challenging imaging conditions.

BioMedInformatics doi: 10.3390/biomedinformatics6030024

Authors: Andrey Timofeev Alexander Anufriev Oleg Ergashev Irina Isakova-Sivak

Background: Hemagglutinin (HA) is the primary surface protein of the influenza A virus, determining its subtype and antigenic properties. Traditional subtype classification methods rely on DNA or amino acid sequence analysis, which does not account for protein spatial folding. Methods: In this work, we propose EpitopeGNN—a graph neural network (GNN) that constructs a residue interaction network (RIN) from the 3D structure of HA and classifies the virus subtype. The model was trained on 249 structures from the Protein Data Bank (PDB), containing H1N1, H3N2, H5N1, and other subtypes. Results: After rigorous sequence redundancy reduction (92% identity), the model maintained 95–100% accuracy on non-redundant data, significantly outperforming sequence-only baselines (the best baseline achieved 85% for multi-class and 92.3% for binary classification). A significant correlation was found between the obtained structural embeddings and phylogenetic distances (r = 0.38, p < 0.001), confirming their biological relevance and opening opportunities for structural monitoring of virus evolution, as well as rapid analog searching for novel strains. Conclusions: We developed a new graph neural network that classifies influenza A virus subtypes directly from the 3D structure of hemagglutinin using residue interaction networks and physicochemical features, which can serve as a foundation for predicting influenza virus receptor specificity and epitope immunogenicity.

BioMedInformatics doi: 10.3390/biomedinformatics6030023

Authors: Youssef Nadir Hicham Esselmani Anass Oukhdouch Habiba Nechchadi Rahma Ennadi Mohammed Amine Lkousse Issame Farouk Mustapha Najimi Mohamed Merzouki

Background: Liver fibrosis arises from chronic liver injury and remains a major clinical challenge due to its progression toward cirrhosis and hepatocellular carcinoma, as well as the absence of approved antifibrotic therapies. This study aimed to characterize the transcriptomic behavior of the EGR1-FOS axis in liver fibrosis and its evolution into hepatocellular carcinoma, and to identify genes shared between liver fibrosis and cirrhosis. Methods: An integrated bioinformatics approach was applied to GEO transcriptomic datasets. Differentially expressed genes in hepatic fibrosis were identified using GSE139602, GSE84044, and GSE49541, with GSE62232 as control when needed, while GSE14323 and GSE89377 were used to detect genes common with cirrhosis. GEPIA, TIMER, and TISCH2 were used to assess the involvement of the EGR1-FOS axis in hepatocellular carcinoma. External validation of EGR1 expression dynamics and its coregulation with FOS was performed using the GSE135251 dataset. Results: Eleven hub genes were identified, with emphasis on the EGR1-FOS axis. EGR1 expression fluctuated across liver fibrosis etiologies, whereas FOS was predominantly downregulated. A strong correlation between EGR1 and FOS (r = 0.77) was observed, remaining stable across fibrosis stages (all p < 0.001) and in hepatocellular carcinoma (r = 0.698, p = 1.81 × 10−55). Despite overall downregulation, both genes increased progressively with advancing fibrosis (EGR1: p = 0.0008–0.0035; FOS: p = 0.0001–0.0188). Four genes were shared between fibrosis and cirrhosis (SOX9, CD24, CXCR4, and CYP2C19). Conclusions: The EGR1-FOS axis acts as a dynamic regulator of liver fibrosis and its progression, and both this axis and the four shared genes identified may serve as valuable biomarkers and potential therapeutic targets.

BioMedInformatics doi: 10.3390/biomedinformatics6020022

Authors: Zineb Sqalli Houssaini Younes Balboul Anas Bouayad

Morocco, facing a growing prevalence of chronic diseases such as diabetes, hypertension, and cardiovascular diseases, must overcome significant challenges to modernize its healthcare system. In this context, the integration of digital technologies, including telemedicine, the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and healthcare system interoperability, represents a promising solution to improve the management of chronic diseases. This article examines how these technologies can be utilized to transform the Moroccan healthcare system into a more accessible, efficient, and patient-focused model of care. The paper reviews recent pilot projects and initiatives, focusing on infrastructure development, remote monitoring, AI and IoMT integration, public health campaigns, and national health programs aimed at improving access to treatment. Building on these observations, the paper explores the potential of an integrated digital health system for managing chronic diseases and proposes a national integrated care architecture that connects Morocco’s public and private healthcare providers. These insights highlight the significance of digital health in Morocco and provide a framework for improved, more patient-centered, and more efficient advanced healthcare. Future perspectives focus on developing an adapted digital transformation approach to further enhance chronic disease management.

BioMedInformatics doi: 10.3390/biomedinformatics6020021

Authors: Junaid Ullah R Kanesaraj Ramasamy Venushini Rajendran

Background: Emergency triage systems using machine learning traditionally rely on structured tabular data (vital signs), creating a “contextual blind spot” that ignores diagnostic information embedded in unstructured clinical narratives. Hybrid AI models that fuse tabular and text data may improve predictive discrimination, but the magnitude and conditions under which fusion adds value remain unclear. Methods: Five databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) were searched from 1 January 2015 to 15 December 2025. Eligible studies employed Hybrid AI models integrating structured and unstructured emergency department data with quantitative baseline comparisons. Twenty-five studies (N ≈ 4.8 million encounters) met inclusion criteria. We extracted marginal performance gains (ΔAUC), calibration metrics, and demographic reporting. Synthesis followed SWiM principles with subgroup meta-regression testing our novel “Complexity Gradient” hypothesis. Results: Hybrid models demonstrated superior discrimination compared to tabular baselines, with effect magnitude dependent on clinical task complexity. Low-complexity tasks (tachycardia prediction) showed minimal gains (median ΔAUC + 0.036, IQR: 0.02–0.05), while high-complexity tasks (hypoxia, sepsis) demonstrated substantial improvement (median ΔAUC + 0.111, IQR: 0.09–0.13). Meta-regression confirmed complexity significantly moderated effect size (R2 = 0.42, p = 0.003). Only 12% (3/25) of studies reported calibration metrics (Brier scores: 0.089–0.142). Zero studies stratified performance by race/ethnicity; 88% (22/25) failed to report training data demographics. Discussion: The complexity gradient framework explains when multimodal fusion adds predictive value: tasks where diagnostic signal resides in narrative features (temporality, negation) rather than physiological measurements. However, systematic absence of calibration reporting and fairness auditing prevents clinical deployment. Seventy-two percent of studies had high risk of bias in the analysis domain due to retrospective designs without temporal validation. Conclusions: Hybrid triage models show promise for complex diagnostic tasks but require mandatory calibration reporting and demographic performance stratification before clinical implementation. We propose minimum reporting standards including Brier scores, race-stratified metrics, and temporal validation protocols.

BioMedInformatics doi: 10.3390/biomedinformatics6020020

Authors: Ammar Saloum Israa Zaher Christian Stipho Enes Demir Varun Naravetla Mehrdad Pahlevani Nasser Yaghi Michael Karsy

Gliomas are generally readily detected and broadly characterized using conventional MRI; however, substantial challenges remain in accurately delineating tumor extent, grading heterogeneous disease, and translating imaging findings into consistent, reproducible clinical decisions. Despite reported Dice coefficients of 0.85–0.91 for whole-tumor segmentation and classification AUC values exceeding 0.90 for glioma grading in curated datasets, most AI systems remain limited by validation design, dataset bias, and inadequate external generalizability. This narrative review synthesizes current AI applications for MRI-based glioma detection and segmentation, highlighting the evolution from radiomics-based classical machine learning approaches relying on handcrafted features to deep learning models capable of end-to-end representation learning. Commonly used MRI sequences, algorithmic paradigms, and reported performance trends are reviewed, with particular emphasis on tumor segmentation as a foundational enabling task. Key limitations that hinder clinical translation are examined, including limited dataset diversity, validation practices that inflate reported performance, domain shift across institutions, acquisition-related bias, and inadequate model interpretability. Emerging strategies to address these challenges, such as multi-institutional training, harmonization techniques, explainable AI frameworks, and workflow-integrated validation, are also discussed. While AI-based models demonstrate strong technical performance in research settings, their clinical impact will depend on rigorous external validation, transparency, and alignment with real-world neuro-oncology workflows.

BioMedInformatics doi: 10.3390/biomedinformatics6020019

Authors: Md. Saymon Hosen Polash Md. Tamim Hasan Saykat Md. Ehsanul Haque Md. Maniruzzaman Mahe Zabin Jia Uddin

Magnetic resonance imaging (MRI) is a critical clinical tool that requires precise and reliable interpretation for effective brain tumor diagnosis and timely treatment planning. Deep learning methods have advanced automated tumor classification greatly in the last few years, but many of the current methods are still challenged by a lack of interpretability, a lack of testing on region-focused data, and a lack of model robustness testing. Such limitations reduce clinical trust and limit the practice of automated diagnostic systems. To address these challenges, this study proposes an interpretable deep learning model for classifying brain tumors using the PMRAM dataset, which is a Bangladeshi brain MRI collection containing four categories: glioma, meningioma, pituitary tumor, and normal brain.. The proposed pipeline combines image preprocessing and feature enhancement methods, and then it trains a series of squeeze-and-excitation (SE)-enhanced convolutional neural networks such as VGG19, DenseNet201, MobileNetV3-Large, InceptionV3, and EfficientNetB3. The SE-enhanced EfficientNetB3 performed best, with 98.70% accuracy, 98.77% precision, 98.70% recall, and 98.70% F1-score. Cross-validation also demonstrated stable performance, with a mean accuracy of 96.89%. The model also exhibited efficient inference with low GPU memory consumption, enabling predictions in about 2–4 s per MRI image. Grad-CAM++ and saliency maps were used to improve the transparency of the results, and it was found that the network was concentrated on the clinically significant parts of the tumor, which affected the model predictions. Further robustness analysis and cross-dataset testing are additional evidence of the generalization possibility of the model. An online application was also implemented to allow real-time prediction and visual explanation of brain tumors. Overall, the proposed framework offers a precise, interpretable, and promising solution to automated brain tumor classification using MRI images.

BioMedInformatics doi: 10.3390/biomedinformatics6020018

Authors: Andrea Marzullo Andrea Quaranta Gerardo Cazzato Cecilia Salzillo

Artificial intelligence (AI) in cardiovascular pathology involves the use of computational models, including machine learning and deep learning (DL), to analyse complex and heterogeneous data. These data include histopathological whole-slide images, cardiovascular imaging techniques such as cardiac magnetic resonance, echocardiography, computed tomography (CT), clinical parameters, and molecular information. The integration of these multimodal data sources allows AI to overcome the limitations of single-modality analysis, improving diagnostic accuracy, prognostic stratification, and personalised clinical decision-making while reducing inter-observer variability. Cardiovascular disease remains the leading cause of mortality worldwide, highlighting the need for more precise and timely diagnostic tools. AI has shown significant promise, particularly in digital pathology, where the digitisation of histological slides combined with advanced algorithms enables improved diagnosis, prognostic assessment, and translational research. This review summarises current AI applications in cardiovascular pathology, focusing on heart transplant rejection, cardiomyopathies, myocarditis, and atherosclerotic and valvular diseases. Automated methods offer important advantages, including diagnostic standardisation, quantitative histological analysis, and improved reproducibility. However, several challenges remain, such as the need for large, well-annotated shared datasets, limited interpretability of AI models, and ethical and legal issues related to clinical implementation. AI represents a promising tool for advancing cardiovascular pathology and personalised medicine, although robust multicentre validation is required before routine clinical adoption.

BioMedInformatics doi: 10.3390/biomedinformatics6020017

Authors: Yong Si Junyi Fan Li Sun Shuheng Chen Elham Pishgar Kamiar Alaei Greg Placencia Maryam Pishgar

Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and accurate prediction of mortality is essential for guiding clinical decision-making and optimizing ICU resource allocation. In this study, we utilized the MIMIC-III database and identified a final analytic cohort of 667 geriatric TBI patients, on which we developed a machine learning framework for 30-day mortality prediction. A rigorous preprocessing pipeline—including Random Forest-based imputation, feature engineering, and hybrid selection—was implemented to refine predictors from 69 to 9 clinically meaningful variables. CatBoost emerged as the top-performing model, achieving an AUROC of 0.867 (95% CI: 0.809–0.922), with a sensitivity of 0.752 and a specificity of 0.888 on the independent test set. SHAP analysis confirmed the importance of the GCS score, oxygen saturation, and prothrombin time as dominant predictors. These findings highlight the potential value of interpretable machine learning tools for early mortality risk stratification in elderly TBI patients and support further validation for future clinical use.

BioMedInformatics doi: 10.3390/biomedinformatics6020016

Authors: Houji Jin Mohammadsaeed Haghi Nausin Kudrot Kamiar Alaei Maryam Pishgar

Postoperative stroke is a serious and fatal condition that often affects elderly surgical patients. This rare but severe complication arises from complex interactions between comorbidities, physiologic instability and demographic disturbances that traditional risk tools often fail to capture.This study aims to develop and validate a machine learning model with an improved ability to predict the risk of postoperative stroke in elderly patients utilising the comprehensive clinical and demographic ICU data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. External validation was performed on MIMIC-III and the eICU Collaborative Research Database, with eICU being the primary validation set. We identified postoperative surgical intensive care unit (SICU) patients aged 55 years or older from all databases. A strict temporal window of the first 24 h of ICU admission was applied across all three datasets while extracting features like laboratory measurements and vital sign summaries in order to ensure that all predictor values were derived from a fixed observation period at the beginning of ICU stay. After preprocessing, applying Multivariate Imputation by Chained Equations (MICE) imputation and initial screening of 88 candidate variables, 20 clinically meaningful predictors were selected through a multistage feature selection pipeline incorporating RFECV and permutation importance. SHAP analysis and LIME analysis were used for interpretability. We evaluated ten machine learning techniques, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNNs), Support Vector Machine (SVM–RBF Kernel), Gradient Boosting (GBDT), Neural Network, XGBoost, CatBoost, Naive Bayes. Among them, Random Forest demonstrated strong predictive performance by achieving an AUROC of 0.8072 (95% CI [0.7890, 0.8253]) on the internal validation set. The model also achieved AUROC of 0.7557 (95% CI [0.7267, 0.7794]) and 0.9144 (95% CI [0.8893, 0.9378]) on the external validation sets eICU and MIMIC-III, respectively. Mean systolic blood pressure, Elixhauser score, minimum calcium, and minimum INR (PT) were consistently identified as the most influential predictors through both SHAP analysis and LIME analysis, thus strengthening model interpretability. Our findings suggest that a Random Forest-based predictive model can provide an accurate and generalisable prediction of postoperative stroke in elderly ICU patients using routinely collected physiologic and laboratory data. This also supports early risk stratification and targeted postoperative monitoring.

BioMedInformatics doi: 10.3390/biomedinformatics6020015

Authors: Adriana Anchía-Alfaro Sebastián Arguedas-Chacón Georgia Hanley-Vargas Sofía Suárez-Sánchez Luis Andrés Aguilar-Castro Sergio Daniel Seas-Azofeifa Kal Che Wong Hsu Diego Quesada-Loría María Felicia Montero-Arias Juliana Salas-Segura Esteban Zavaleta-Monestel

Background/Objectives: AI-based tools for chest radiograph interpretation are increasingly used as decision-support systems, yet their performance must be validated in local clinical environments before deployment. This study evaluated the diagnostic performance of qXR (Qure.ai, v3.2) for detecting pulmonary nodules, cardiomegaly, and pleural effusion in adult patients at Hospital Clínica Bíblica, San José, Costa Rica. Methods: Three radiologists independently interpreted 225 chest radiographs, providing the reference standard. qXR outputs were compared against radiologist assessments for each finding. The sensitivity, specificity, Cohen’s kappa, and area under the ROC curve (AUC) were calculated. Due to the convenience-stratified sampling design, predictive values were not used for clinical interpretation. Results: For pulmonary nodules, qXR achieved a sensitivity of 0.71, specificity of 0.90, Cohen’s kappa of 0.51, and AUC of 0.80. For pleural effusion, sensitivity and specificity were both 0.86, with a kappa of 0.63 and AUC of 0.86. Cardiomegaly showed the lowest agreement, with a sensitivity of 0.64, specificity of 0.91, kappa of 0.57, and AUC of 0.77. Conclusions: qXR demonstrated moderate diagnostic agreement with radiologist assessments for pulmonary nodules and pleural effusion, and lower agreement for cardiomegaly under local imaging conditions. These results reflect technical concordance between the AI system and individual radiologists and do not constitute evidence of clinical utility or real-world impact. Context-specific validation is essential prior to integrating AI tools into routine radiological workflows.

BioMedInformatics doi: 10.3390/biomedinformatics6020014

Authors: Ebtihal Kamal Ehssan Moglad

Background: Collagen type V alpha 2 (COL5A2) is an important regulator of tumor progression and metastasis in various tumors. microRNAs (miRNAs), key post-transcriptional regulators of gene expression, can act as tumor suppressors or oncogenes. Dysregulated miRNA is closely associated with tumor development and progression. This study aimed to investigate COL5A2 expression across different tumors and to investigate its prognostic, immune cell infiltration, and miRNA associations. Methods: We used the TIMER database to assess COL5A2 expression across various tumor types and tumor-infiltrating immune cells. The UALCAN database was used to study the associations between COL5A2 expression and tumor stages, while overall survival results were obtained using the Kaplan–Meier plotter. We identified tumor suppressor miRNAs predicted to regulate COL5A2 expression in different tumors using the miRNet database and evaluated correlations between their expression levels, COL5A2 expression, and patient survival using the StarBase database. Results: COL5A2 was significantly upregulated in 12 tumors, and the upregulated COL5A2 expression was associated with altered immune cell infiltration and worse overall survival in lung and stomach adenocarcinoma. A total of 29 tumor suppressor miRNAs were identified as potential regulators of COL5A2 expression. We found that hsa-miR-101-3p and hsa-miR-29c-3p were downregulated in lung adenocarcinoma and negatively correlated with COL5A2 expression, and their downregulated expression was associated with unfavorable prognosis. Conclusions: COL5A2 and its regulatory miRNAs, hsa-miR-101-3p and hsa-miR-29c-3p, may represent potential diagnostic and prognostic biomarkers and modulators of the tumor immune microenvironment in lung adenocarcinoma. These results warrant further experimental validation and future evaluation in the context of Sustainable Development Goal (SDG) 3-aligned cancer control strategies.

BioMedInformatics doi: 10.3390/biomedinformatics6020013

Authors: David Jackson Athanasios Gousiopoulos Theodoros G. Soldatos

Scientific evidence is fundamental to uncovering insights about health, including food and nutritional claims. Substantiating such claims requires robust scientific procedures that often include clinical studies, biochemical analyses, and the examination of multiple forms of data. The growing capabilities of artificial intelligence (AI) and large language models (LLMs) present new opportunities for analyzing food–health relationships and supporting health claim validation. Yet, applying these technologies to the food and nutrition domain raises challenges that differ from those encountered in broader biomedical text mining (TM). In this perspective, we review key issues, including the complexity and heterogeneity of food-related data, the scarcity of food-specific language models and standardized resources, difficulties in interpreting nuanced and often contradictory evidence, and requirements for integrating AI tools into regulatory workflows. We compare modern LLM approaches with traditional TM methods and discuss how each may complement the other. Our position is that, despite their promise, current AI and LLM tools cannot yet reliably handle the subtleties of food–health evidence without substantial domain-specific refinement and human expert oversight. We advocate for hybrid approaches that combine the precision of established TM techniques with the analytical breadth of LLMs, supported by harmonized ontologies, multidimensional evaluation frameworks, and human-in-the-loop validation, particularly in regulatory contexts. We also highlight the importance of public education, transparent communication standards, and coordinated cross-disciplinary efforts to ensure these technologies serve broader goals of food safety, consumer trust, and global health.

BioMedInformatics doi: 10.3390/biomedinformatics6020012

Authors: Amir Azadnouran Hesam Akbari Muhammad Tariq Sadiq Daniella Smith Mutlu Mete

Background: Parkinson’s disease is a progressive neurodegenerative disorder. Early PD detection plays a key role in effective therapy. Electroencephalography is a neuroimaging technique used to analyze brain abnormalities, such as those seen in patients with PD. However, the complex nature of EEG data requires advanced signal processing and classification methods. Methods: This study systematically evaluates three time-frequency (TF) representation techniques, namely discrete wavelet transform (DWT), continuous wavelet transform (CWT), and synchrosqueezing transform (SST), along with four pretrained convolutional neural network architectures for EEG-based PD detection. The experiments were performed using the San Diego dataset. Image-wise and subject-wise 5-fold cross-validation were employed to assess performance and generalization capability. Results: CWT and SST consistently outperform DWT across all evaluated architectures in image-wise CV evaluation. At the image-wise level, the CWT-EfficientNet-B0 model achieved 97.28% accuracy for HC vs. PD-OFF classification, while SST-EfficientNet-B0 reached 97.26% accuracy for HC vs. PD-ON classification. In subject-wise evaluation, acceptable accuracies of up to 84% were achieved, indicating the ability of the framework in learning PD patterns for unseen subjects. Conclusions: These findings demonstrate that the choice of TF representation has a strong impact on classification performance and that lightweight CNN architectures can achieve high image-wise accuracy with reduced computational cost.

BioMedInformatics doi: 10.3390/biomedinformatics6020011

Authors: Amirhosein Panjipour Soheil Sojdeh Zohreh Arabpour Ali R. Djalilian

Conventional topical therapy for corneal and anterior segment diseases is limited by rapid tear clearance and multilayer corneal barriers, resulting in low bioavailability and the need for frequent dosing. Artificial intelligence (AI) is emerging as a complementary approach that learns quantitative relationships between molecular structure, formulation variables, and ocular performance. In corneal drug delivery, machine learning models have been used to optimize multicomponent formulations and processing conditions; predict key quality attributes such as particle size, zeta potential, encapsulation efficiency and release kinetics; and estimate corneal permeability, retention and ocular irritation risk, thereby reducing experimental burden and guiding safer design. AI can also be coupled with mechanistic ocular pharmacokinetic/pharmacodynamic models to translate formulation attributes into predicted tissue exposure. Finally, inverse design approaches enable the discovery of new carriers and devices, illustrated by machine learning-guided peptide carriers and smart contact lens platforms that combine sensing with on-demand drug release. Despite these advances, current datasets remain small and heterogeneous, external validation and benchmarking against conventional workflows are limited, and uncertainty quantification and interpretability must be addressed to enable clinical translation. This review summarizes corneal barriers and delivery platforms, critically evaluates where AI provides measurable value across design, characterization and performance and highlights data and validation priorities needed for trustworthy AI-enabled corneal therapeutics.

BioMedInformatics doi: 10.3390/biomedinformatics6020010

Authors: Alexandre G. de Brevern

BioMedInformatics is an international, peer-reviewed, open access journal that covers all areas of biomedical informatics, computational biology, and medicine. Established in 2021, the journal is now five years old and reflects the evolution of the field through its consistent thematic focus on Artificial Intelligence (AI)-driven diagnosis and prediction, with a particular emphasis on translational clinical decision support and biomedical signal and imaging analysis. Despite the predominance of AI-related topics, classical bioinformatics remains a major focus, with a particular emphasis on the discovery of biomarkers and the development of data resources. This editorial summarises this evolution, which accurately reflects the field as a whole.

BioMedInformatics doi: 10.3390/biomedinformatics6010009

Authors: Gabriela Georgieva Panayotova

Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and establish a unified framework for software maturity. This narrative review addresses this gap by synthesizing representative software ecosystems across three major pillars: bioinformatics, molecular modeling/simulations, and epidemiology/public health. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST+ (v2.16.0, NCBI, Bethesda, MD, USA), Bioconductor (v3.20, Bioconductor Foundation, Seattle, WA, USA), AutoDock Vina (v1.2.5, Scripps Research, La Jolla, CA, USA), GROMACS (v2024.3, GROMACS Team, Stockholm, Sweden), Epi Info (v7.2.6, CDC, Atlanta, GA, USA), QGIS (v3.40, QGIS.org, Gossau, Switzerland), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning.

BioMedInformatics doi: 10.3390/biomedinformatics6010008

Authors: Achraf Jallaglag My Abdelouahed Sabri Ali Yahyaouy Abdellah Aarab

Background: Depression is a common mental disorder, and early and objective diagnosis of depression is challenging. New advances in deep learning show promise for processing audio and video content when screening for depression. Nevertheless, the majority of current methods rely on raw video processing or multimodal pipelines, which are computationally costly and challenging to understand and create privacy issues, restricting their use in actual clinical settings. Methods: Based solely on spatiotemporal 3D face landmark representations, we describe a unique, totally visual, and lightweight deep learning approach to overcome these constraints. In this paper we introduce, for the first time, a pure visual deep learning framework, based on spatiotemporal 3D facial landmarks extracted from clinical interview videos contained in the DAIC-WOZ and Extended DAIC-WOZ (E-DAIC) datasets. Our method does not use raw video or any type of semi-automated multimodal fusion. Whereas raw video streaming can be computationally expensive and is not well suited to investigating specific variables, we first take a temporal series of 3D landmarks, convert them to pseudo-images (224 × 224 × 3), and then use them within a CNN-LSTM framework. Importantly, CNN-LSTM provides the ability to analyze the spatial configuration and temporal dimensions of facial behavior. Results: The experimental results indicate macro-average F1 scores of 0.74 on DAIC-WOZ and 0.762 on E-DAIC, demonstrating robust performance under heavy class imbalances, with a variability of ±0.03 across folds. Conclusion: These results indicate that landmark-based spatiotemporal modeling represents the future of lightweight, interpretable, and scalable automatic depression detection. Second, our results suggest exciting opportunities for completely embedding ADI systems within the framework of real-world MHA.

BioMedInformatics doi: 10.3390/biomedinformatics6010007

Authors: Mohammed A. Atiea Mona Gafar Shahenda Sarhan Abdullah M. Shaheen

Artificial intelligence (AI) has shown promising performance in brain tumor diagnosis and prognosis; however, most reported advances remain difficult to translate into clinical practice due to limited interpretability, inconsistent evaluation protocols, and weak generalization across datasets and institutions. In this work, we present a critical synthesis of recent brain tumor AI studies (2020–2025) guided by two novel conceptual tools: a unified diagnostic-prognostic framework and a triadic evaluation model emphasizing interpretability, computational efficiency, and generalizability as core dimensions of clinical readiness. Following PRISMA 2020 guidelines, we screened and analyzed over 100 peer-reviewed studies. A structured analysis of reported metrics reveals systematic trends and trade-offs—for instance, between model accuracy and inference latency—rather than providing a direct performance benchmark. This synthesis exposes critical gaps in current evaluation practices, particularly the under-reporting of interpretability validation, deployment-level efficiency, and external generalization. By integrating conceptual structuring with evidence-driven analysis, this work provides a framework for more clinically grounded development and evaluation of AI systems in neuro-oncology.

BioMedInformatics doi: 10.3390/biomedinformatics6010006

Authors: Princy Randhawa Veerendra Nath Jasthi Kumar Piyush Gireesh Kumar Kaushik Malathy Batamulay S. N. Prasad Manish Rawat Kiran Veernapu Nithesh Naik

The lack of clinical data for chronic kidney disease (CKD) prediction frequently results in model overfitting and inadequate generalization to novel samples. This research mitigates this constraint by utilizing a Conditional Tabular Generative Adversarial Network (CTGAN) to enhance a constrained CKD dataset sourced from the University of California, Irvine (UCI) Machine Learning Repository. The CTGAN model was trained to produce realistic synthetic samples that preserve the statistical and feature distributions of the original dataset. Multiple machine learning models, such as AdaBoost, Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were assessed on both the original and enhanced datasets with incrementally increasing degrees of synthetic data dilution. AdaBoost attained 100% accuracy on the original dataset, signifying considerable overfitting; however, the model exhibited enhanced generalization and stability with the CTGAN-augmented data. The occurrence of 100% test accuracy in several models should not be interpreted as realistic clinical performance. Instead, it reflects the limited size, clean structure, and highly separable feature distributions of the UCI CKD dataset. Similar behavior has been reported in multiple previous studies using this dataset. Such perfect accuracy is a strong indication of overfitting and limited generalizability, rather than feature or label leakage. This observation directly motivates the need for controlled data augmentation to introduce variability and improve model robustness. The dataset with the greatest dilution, comprising 2000 synthetic cases, attained a test accuracy of 95.27% utilizing a stochastic gradient boosting approach. Ensemble learning techniques, particularly gradient boosting and random forest, regularly surpassed conventional models like KNN in terms of predicted accuracy and resilience. The results demonstrate that CTGAN-based data augmentation introduces critical variability, diminishes model bias, and serves as an effective regularization technique. This method provides a viable alternative for reducing overfitting and improving predictive modeling accuracy in data-deficient medical fields, such as chronic kidney disease diagnosis.

BioMedInformatics doi: 10.3390/biomedinformatics6010005

Authors: Cunyuan Luan Huabo Liu

Evaluating the difficulty of endotracheal intubation during pre-anesthesia assessment has consistently posed a challenge for clinicians. Accurate prediction of intubation difficulty is crucial for subsequent treatment planning. However, existing diagnostic methods often suffer from low accuracy. To tackle this issue, this study presented an automated airway classification method utilizing Convolutional Neural Networks (CNNs). We proposed Adaptive Attention DenseNet for Laryngeal Ultrasound Classification (AdaDenseNet-LUC), a network architecture that enhances classification performance by integrating an adaptive attention mechanism into DenseNet (Dense Convolutional Network), enabling the extraction of deep features that aid in difficult airway classification. This model associates laryngeal ultrasound images with actual intubation difficulty, providing healthcare professionals with scientific evidence to help improve the accuracy of clinical decision-making. Experiments were performed on a dataset of 1391 ultrasound images, utilizing 5-fold cross-validation to assess the model’s performance. The experimental results show that the proposed method achieves a classification accuracy of 87.41%, sensitivity of 86.05%, specificity of 88.59%, F1 score of 0.8638, and AUC of 0.94. Grad-CAM visualization techniques indicate that the model’s attention is attention to the tracheal region. The results demonstrate that the proposed method outperforms current approaches, delivering objective and accurate airway classification outcomes, which serve as a valuable reference for evaluating the difficulty of endotracheal intubation and providing guidance for clinicians.

BioMedInformatics doi: 10.3390/biomedinformatics6010004

Authors: Yupeng Li Junyi Fan Kamiar Alaei Maryam Pishgar

Background: Sepsis-associated liver injury (SALI) is a serious complication of sepsis that increases the risk of organ dysfunction and mortality; however, early identification of high-risk patients remains difficult due to nonspecific clinical features and complex pathophysiology. This study aimed to develop machine learning (ML) models to predict 28-day mortality in SALI patients within the first 24 h of intensive care unit (ICU) admission. Methods: A total of 1157 patients were included, comprising 826 from the MIMIC-IV (v2.2) database, 225 from MIMIC-III (v1.4), and 106 from eICU (v2.0). Data from MIMIC-IV were split into training and internal validation sets (7:3), while MIMIC-III and eICU served as external validation cohorts. Thirty clinically relevant features were selected. Eight ML models were evaluated using AUROC, accuracy, precision, recall, F1-score, and specificity. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) enhanced interpretability. Results: XGBoost model achieved the best performance, with an AUROC of 0.8556 (95% CI: 0.807–0.898), accuracy of 0.7702, recall of 0.8469, and specificity of 0.7200. SHAP identified lactate, blood urea nitrogen, heart rate, hemoglobin, and diastolic blood pressure as key predictors, while LIME provided patient-level interpretability. Conclusions: The XGBoost-based model may facilitate early mortality risk stratification and support clinical decision-making for SALI patients in ICU settings.

BioMedInformatics doi: 10.3390/biomedinformatics6010003

Authors: Mounir Salhi Moulay A. Akhloufi

Chest X-ray radiology report generation is a challenging task that involves techniques from medical natural language processing and computer vision. This paper provides a comprehensive overview of recent progress. The annotation protocols, structure, linguistic characteristics, and size of the main public datasets are presented and compared. Understanding their properties is necessary for benchmarking and generalization. Both clinically oriented and natural language generation metrics are included in the model evaluation strategies to assess their performance. Their respective strengths and limitations are discussed in the context of radiology applications. Recent deep learning approaches for report generation and their different architectures are also reviewed. Common trends such as instruction tuning and the integration of clinical knowledge are also considered. Recent works show that current models still have limited factual accuracy, with a score of 72% reported with expert evaluations, and poor performance on rare pathologies and lateral views. The most important challenges are the limited dataset diversity, weak cross-institution generalization, and the lack of clinically validated benchmarks for evaluating factual reliability. Finally, we discuss open challenges related to data quality, clinical factuality, and interpretability. This review aims to support researchers by synthesizing the current literature and identifying key directions for developing more clinically reliable report generation systems.

BioMedInformatics doi: 10.3390/biomedinformatics6010002

Authors: Enric Carcereny Araceli Lopez Mireia Coma Carlos Ponce Laura Buxó Anna Martinez-Cardús

Background/Objectives: Brain metastases (BM) are a major challenge in the treatment of non-small cell lung cancer (NSCLC), particularly among patients with anaplastic lymphoma kinase rearrangements (ALK+ NSCLC), where incidence can reach up to 60% during the course of the disease. This study used in silico systems biology and artificial intelligence-based modeling to investigate the mechanistic effects of brigatinib, a second-generation ALK inhibitor, on metastatic processes in both primary tumors (PT) and established BM. Methods: We applied the Therapeutic Performance Mapping System (TPMS) technology, which integrates systems biology and artificial intelligence, to simulate the impact of brigatinib on metastasis-associated pathways in PT and BM of ALK+ NSCLC patients. Results: In these simulations, brigatinib was predicted to modulate a broad set of proteins implicated in metastasis in both PT and BM, acting mainly through IGF1R, EGFR, FLT3, and ROS1, in addition to its known target ALK. Conclusions: These results suggest brigatinib’s potential to impact key pathways involved in metastatic progression and intracranial disease control. Overall, this study provides insights into brigatinib’s multifaceted role in targeting metastatic processes in ALK+ NSCLC, underscoring its potential benefits in both PT and BM. Nonetheless, further experimental and clinical studies would confirm our results and the potential of in silico models reported here.

BioMedInformatics doi: 10.3390/biomedinformatics6010001

Authors: Fadilah Fadilah Hartanti Dian Ikawati Anis Karuniawati Linda Erlina Fitria Agustina Rafika Indah Paramita Mohd Azrul Naim Mohamad

Acne vulgaris (AV) is a common dermatological disorder in adolescents, encompassing both non-inflammatory and inflammatory lesions, with growing evidence implicating the skin microbiome in its pathogenesis. This study analyzed skin lesion samples from 12 adolescents with AV using 16S rRNA high-throughput sequencing, with 12 healthy skin microbiome datasets as references. A total of 4.7 million high-quality reads were obtained, yielding 765,211 clean reads clustered into 1013 operational taxonomic units (OTUs). Microbial communities in lesions differed markedly from those in healthy skin. At the phylum level, lesions showed higher proportions of Bacteroidota and Bacillota, whereas healthy skin was dominated by Actinobacteria. At the genus level, lesions were modestly but significantly higher in Staphylococcus, Corynebacterium, and Peptoniphilus, while Cutibacterium was more abundant in healthy skin. Alpha diversity analysis revealed greater species richness and phylogenetic diversity in healthy skin, but higher evenness in lesions. Beta diversity confirmed significant differences in community structure. Functional prediction identified 391 metabolic pathways, 163 of which differed significantly; only three were enriched in lesions, while 160 were more abundant in healthy skin. Lipase activity was elevated in lesions, whereas hyaluronate lyase activity was higher in healthy skin. These findings indicate that healthy skin supports a richer and more functionally diverse microbial metabolism, whereas acne lesions are associated with reduced metabolic capabilities. Overall, the acne lesion microbiome exhibits reduced diversity, altered bacterial composition, and distinct functional traits compared to healthy skin, underscoring the role of microbial imbalance in acne and suggesting potential microbial targets for treatment.

BioMedInformatics doi: 10.3390/biomedinformatics5040072

Authors: Simone Karine da Costa Mesquita Luana Souza Freitas Isabelle Pereira da Silva Anna Alice Carmo Gonçalves Alcides Viana de Lima Neto Carlos Alberto de Albuquerque Silva Nielsen Castelo Damasceno Dantas Rhayssa de Oliveira e Araújo Isabelle Katherinne Fernandes Costa

Background: Due to the complexity of venous ulcer treatment, the role of nurses is critical, and artificial intelligence, particularly artificial neural networks of the Multilayer Perceptron type, can be effective tools that support professionals with objective, real-time evaluation. Thus, the present study aims to develop a network to assist in nurse decision-making regarding topical therapies for the treatment of venous ulcers. Methods: A methodological study with a technological focus and quantitative approach was conducted. The Unified Process methodology model was used, based on the Rational Unified Process strategy, following four phases: conception, elaboration, construction, and transition. Results: The development of the artificial neural network involved the collaboration of three specialists who evaluated clinical cases and images of venous ulcers to identify the topical therapies used in their clinical practice. A total of 23 dressings were selected, studied, and grouped into evaluation protocols to create the neural network flowchart, which defined the structure of the network. This network was then used by 13 nurses through the VenoTEC app (version 1.2, developed by the authors, Natal, Brazil). Conclusions: The software developed showed promising results in the initial evaluations conducted. The network achieved the highest accuracy in the initial tests and received a very good usability rating from the nurses who participated in the evaluation. The small dataset limits the generalization capability of the findings. Further studies are needed with additional datasets and populations.

BioMedInformatics doi: 10.3390/biomedinformatics5040071

Authors: Junyi Fan Li Sun Shuheng Chen Yong Si Minoo Ahmadi Maryam Pishgar

Vancomycin remains a cornerstone for severe Gram-positive infections in the ICU, yet creatinine elevation—a sensitive marker of early renal stress—occurs frequently and complicates therapy. We developed a machine learning model to predict vancomycin-associated creatinine elevation using routinely available clinical data, enabling preemptive risk stratification. In this retrospective MIMIC-IV cohort study (n=10,288 ICU adults aged 18–80 receiving vancomycin), the primary outcome was creatinine elevation per KDIGO criteria (≥0.3 mg/dL within 48 h or ≥50% within 7 d). A two-stage feature selection (SelectKBest + Random Forest) identified 15 predictors from 30 candidates. Six algorithms were compared via 5-fold cross-validation. CatBoost was selected for final modeling; interpretability was assessed using SHAP values and Accumulated Local Effects (ALE) plots. Creatinine elevation occurred in 2903 patients (28.2%). CatBoost achieved AUROC 0.818 (95% CI: 0.801–0.834), sensitivity 0.800, specificity 0.681, and NPV 0.900. Top predictors were serum phosphate, total bilirubin, magnesium, Charlson Comorbidity Index, and APSIII score. SHAP analysis confirmed hyperphosphatemia as the strongest driver; ALE plots revealed non-linear, clinically plausible thresholds (e.g., phosphate >4.5 mg/dL sharply increased risk). This interpretable model accurately predicts vancomycin-associated creatinine elevation using standard ICU monitoring data. With high negative predictive value, it supports early exclusion of low-risk patients and targeted interventions (e.g., intensified TDM, nephrotoxin avoidance) in high-risk cases—facilitating precision antimicrobial stewardship in critical care.

BioMedInformatics doi: 10.3390/biomedinformatics5040070

Authors: Larissa Pusch Tim O. F. Conrad

Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate information not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then executed on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user-interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025.

BioMedInformatics doi: 10.3390/biomedinformatics5040069

Authors: Duaa Abuarqoub Mahdi Mutahar

Artificial intelligence (AI) is rapidly emerging as a transformative tool capable of addressing critical challenges and improving outcomes in tissue engineering and regenerative medicine. This paper demonstrates how machine learning and data fusion predict stem cell activity and potency, improve cellular characterization, and optimize therapeutic design. It also highlights important uses of AI in tissue engineering and cell-based therapeutics. By enabling accurate, non-invasive, and quantitative examination of living cells, AI also advances microscopy and imaging, facilitating better decision-making and real-time monitoring. Using search criteria including artificial intelligence, machine learning, deep learning, regenerative medicine, stem cells, and tissue engineering, the review was carried out using PubMed, Scopus, Web of Science, and Google Scholar. A total of 71 articles were screened; 8 non-peer-reviewed sources, 5 conference abstracts, and 4 duplicates were excluded. The final dataset included 7 clinical studies, 6 preclinical investigations, 18 original research articles, and 23 review papers. AI techniques, datasets, performance indicators, and regeneration results were compiled in the extracted data. To summarize, AI speeds up the development of tissue engineering, minimizes trial-and-error experimentation, lowers research expenses, forecasts tissue interactions, and enhances scaffold and biomaterial design. Consequently, AI integration enhances stem cell-based treatments and regenerative approaches, underscoring the necessity of interdisciplinary cooperation and ongoing technical development.

BioMedInformatics doi: 10.3390/biomedinformatics5040068

Authors: Mohammed Addou El Bekkaye Mermri Mohammed Gabli

Modern diagnostic systems face computational challenges when processing exponential disease-symptom combinations, with traditional approaches requiring up to 2n evaluations for n symptoms. This paper presents MARS (Matrix-Accelerated Reasoning System), a diagnostic framework combining Case-Based Reasoning with matrix representations and intelligent filtering to address these limitations. The approach encodes disease-symptom relationships as matrices enabling parallel processing, implements adaptive rule-based filtering to prioritize relevant cases, and features automatic rule generation with continuous learning through a dynamically updated Pertinence Matrix. MARS was evaluated on four diverse medical datasets (41 to 721 diseases) and compared against Decision Tree, Random Forest, k-Nearest Neighbors, Support Vector Classifier, Bayesian classifiers, and Neural Networks. On the most challenging dataset (721 diseases, 49,365 test cases), MARS achieved the highest accuracy (87.34%) with substantially reduced processing time. When considering differential diagnosis, accuracy reached 98.33% for top-5 suggestions. These results demonstrate that MARS effectively balances diagnostic accuracy, computational efficiency, and interpretability, three requirements critical for clinical deployment. The framework’s ability to provide ranked differential diagnoses and update incrementally positions it as a practical solution for diverse clinical settings.

BioMedInformatics doi: 10.3390/biomedinformatics5040067

Authors: Tasnim Abbasi Lubna Pinky

Background/Objectives: This review paper summarizes and critically analyzes different Machine Learning (ML) and Artificial Intelligence (AI)-based predictive modeling techniques in early detection and personalized treatment for Kidney diseases, specifically diabetic kidney disease (DKD), chronic kidney disease (CKD), and end-stage renal disease (ESRD). This manuscript focuses on integrating electronic medical record (EMR) data with multi-omics biomarkers to enhance clinical decision-making. Method: A systematic database search retrieved 43 peer-reviewed articles from PubMed, Google Scholar, and ScienceDirect. These works were critically analyzed based on methodological rigor, model interpretability, and translational potential. Review: This paper examined a series of advanced AI and ML models, including but not limited to Random Forests (RF), Extreme Gradient Boosting (XGBoost), deep neural networks, and artificial neural networks, among others. Additionally, this paper explicitly explored validated approaches for fibrosis staging, dialysis prediction, and mortality risk assessment. Conclusions: The paper shows how leveraging AI models for patient-specific biomarker and EMR data presents substantial promise for facilitating preventative interventions, guiding timely nephrology referrals, and optimizing individualized treatment regimens. These state-of-the-art tools will ultimately improve long-term renal outcomes and reduce healthcare burdens. The study further addresses ethical challenges and potential adverse implications associated with the use of AI in clinical settings.

BioMedInformatics doi: 10.3390/biomedinformatics5040066

Authors: Maria Cuevas-Nunez Cosimo Galletti Luca Fiorillo Aida Meto Wilmer Rodrigo Díaz-Castañeda Shokoufeh Shahrabi Farahani Guido Fadda Valeria Zuccalà Victor Gil Manich Javier Bara-Casaus Maria-Teresa Fernández-Figueras

Background: Salivary gland tumors pose a diagnostic challenge due to their histological heterogeneity and overlapping features. While immunohistochemistry (IHC) is critical for accurate classification, selecting appropriate markers can be subjective and influenced by resource availability. Artificial intelligence (AI), particularly large language models (LLMs), may support diagnostic decisions by recommending IHC panels. This study evaluated the performance of ChatGPT-4, a free and widely accessible general-purpose LLM, in recommending IHC markers for salivary gland tumors. Methods: ChatGPT-4 was prompted to generate IHC recommendations for 21 types of salivary gland tumors. A consensus of expert pathologists established reference panels. Each tumor type was queried using a standardized prompt designed to elicit IHC marker recommendations (“What IHC markers are recommended to confirm a diagnosis of [tumor type]?”). Outputs were assessed using a structured scoring rubric measuring accuracy, completeness, and relevance. Agreement was measured using Cohen’s Kappa, and diagnostic performance was evaluated via sensitivity, specificity, and F1-scores. Repeated-measures ANOVA and Bland–Altman analysis assessed consistency across three prompts. Results were compared to a rule-based system aligned with expert protocols. Results: ChatGPT-4 demonstrated moderate overall agreement with the pathologist panel (κ = 0.53). Agreement was higher for benign tumors (κ = 0.67) than for malignant ones (κ = 0.40), with pleomorphic adenoma showing the strongest concordance (κ = 0.74). Sensitivity values across tumor types ranged from 0.25 to 0.96, with benign tumors showing higher sensitivity (>0.80) and lower specificity (<0.50) observed in complex malignancies. The overall F1-score was 0.84 for benign and 0.63 for malignant tumors. Repeated prompts produced moderate variability without significant differences (p > 0.05). Compared with the rule-based system, ChatGPT included more incorrect and missed markers, indicating lower diagnostic precision. Conclusions: ChatGPT-4 shows promise as a low-cost tool for IHC panel selection but currently lacks the precision and consistency required for clinical application. Further refinement is needed before integration into diagnostic workflows.

BioMedInformatics doi: 10.3390/biomedinformatics5040065

Authors: Aftab Alam Syed Sikandar Shah Syed Arman Rabbani Mohamed El-Tanani

Artificial Intelligence (AI) is reshaping pharmacy practice by enhancing decision-making, personalizing therapy, and improving medication safety. AI applications now span drug discovery, clinical decision support, and adherence monitoring. This narrative review explores key innovations, practical applications, and the implications of AI integration in pharmacy practice, with a focus on emerging tools, pharmacist roles, and ethical considerations. The review was conducted using literature from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Thematic synthesis included AI-based drug interaction checkers, Clinical Decision Support Systems (CDSS), telepharmacy, pharmacogenomics, and predictive analytics. AI enhances clinical decision-making, reduces medication errors, and supports precision medicine. AI tools support pharmacists and healthcare professionals in optimizing care. However, data privacy, algorithmic bias, and workflow integration continue to pose challenges. AI holds transformative potential in pharmacy, though its integration requires overcoming ethical and workflow-related challenges. Ethical and regulatory vigilance, coupled with pharmacist training and interdisciplinary collaboration, is essential to realize the full potential of AI.

BioMedInformatics doi: 10.3390/biomedinformatics5040064

Authors: Ibrahim Moubarak Nchouwat Ndumgouo Mohammad Zahir Uddin Chowdhury Stephanie Schuckers

Background: Dopamine (DA) is a key biomarker for neurodegenerative diseases such as Parkinson’s. However, detailed insights into how DA release in the brain changes with aging remain challenging. Integrating machine learning with DA sensing platforms has proven more effective in tracking age-dependent DA dynamics than using the sensing platforms alone. Method: This study presents a machine learning framework to automatically detect and quantify dopamine (DA) release using the near-infrared catecholamine nanosensors (nIRCats) dataset of acute mouse brain tissue across three age groups (4, 8.5, and 12 weeks), focusing on the dorsolateral (DLS) and dorsomedial striatum (DMS). 251 image frames from the dataset were analyzed to extract features for training a CatBoost regression model. To enhance speed while maintaining much of the predictive accuracy, the model was distilled into a kernelized Ridge regression model. Results: The model achieved validation Mean Squared Error (MSE) of 0.004 and R2 value of 0.79. When the acceptable prediction range was expanded to include values within ±10% of the actual DA release and mouse age, model performance improved to a validation MSE of 0.001 and R2 value of 0.97. Conclusions: These results demonstrate that the proposed approach can accurately and automatically predict spatial and age-dependent dopamine dynamics; a crucial requirement for optimizing deep brain stimulation therapies for neurodegenerative disorders such as Parkinson’s disease (PD) and depression.

BioMedInformatics doi: 10.3390/biomedinformatics5040063

Authors: Yixuan Zhu Mahmoud Elbattah

Background/Objectives: Endometriosis is a chronic inflammatory condition that often requires laparoscopic examination for definitive diagnosis. Automated analysis of laparoscopic images using Deep Learning (DL) may support clinicians by improving diagnostic consistency and efficiency. This study aimed to develop and evaluate explainable DL models for the binary classification of endometriosis using laparoscopic images from the publicly available GLENDA (Gynecologic Laparoscopic ENdometriosis DAtaset). Methods: Four representative architectures—ResNet50, EfficientNet-B2, EdgeNeXt_Small, and Vision Transformer (ViT-Small/16)—were systematically compared under class-imbalanced conditions using five-fold cross-validation. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) were applied for visual explanation, and their quantitative alignment with expert-annotated lesion masks was assessed using Intersection over Union (IoU), Dice coefficient, and Recall. Results: Among the evaluated models, EdgeNeXt_Small achieved the best trade-off between classification performance and computational efficiency. Grad-CAM produced spatially coherent visualizations that corresponded well with clinically relevant lesion regions. Conclusions: The study shows that lightweight convolutional neural network (CNN)–Transformer architectures, combined with quantitative explainability assessment, can identify endometriosis in laparoscopic images with reasonable accuracy and interpretability. These findings indicate that explainable AI methods may help improve diagnostic consistency by offering transparent visual cues that align with clinically relevant regions. Further validation in broader clinical settings is warranted to confirm their practical utility.

BioMedInformatics doi: 10.3390/biomedinformatics5040062

Authors: Naiany Tenório Maria Gabriela Amaral Lima Herbert Albérico de Sá Leitão Diego Dantas

Introduction: The digital era has provided the development of innovative health devices that enable the precise characterization of health and disease, facilitating diagnoses and interventions. This study aimed to systematically review and verify the quality of mobile applications (apps) available for the monitoring and assessment of breast cancer-related lymphedema (BCRL). Methods: A systematic search was conducted in the Apple App Store and Google Play Store for apps related to BCRL monitoring and assessment. Two independent reviewers extracted descriptive data and evaluated app quality using the validated User Mobile App Rating Scale (uMARS). Results: Out of 630 apps screened, four met the inclusion criteria and were analyzed. Two Korean apps targeted patients, providing educational content, self-assessment tools, and bilingual interfaces. Two British apps, LymVol and LymphaTech Lite, focused on volumetric measurement and clinical use, although LymVol lacked compatibility with recent Android versions. Quality assessment using the uMARS indicated that the included applications performed consistently across the evaluated domains, despite low download numbers and the absence of user ratings. Conclusions: Although mobile apps have the potential to enhance lymphedema monitoring and assessment, more accessible and scientifically validated tools are needed to ensure effective use by healthcare professionals and patients. Developers are encouraged to create accessible, linguistically inclusive smartphone apps that incorporate standardized assessment protocols and regular updates to ensure usability and accuracy. Rigorous validation studies covering reproducibility, diagnostic accuracy, and real-world clinical outcomes should be conducted by researchers to guarantee safety and reliability.

BioMedInformatics doi: 10.3390/biomedinformatics5040061

Authors: Dimitria Theophanis Boukouvalas Márcia Aparecida Silva Bissaco Humberto Dellê Alessandro Melo Deana Peterson Adriano Belan Sidnei Alves de Araújo

Background: The growing demand for automated microorganism classification in the context of Laboratory 4.0 highlights the potential of convolutional neural networks (CNNs) for accurate and efficient image analysis. However, their effectiveness remains limited by the scarcity of large, labeled datasets. This study addresses a key gap in the literature by investigating how commonly used image preprocessing techniques, such as lossy compression, non-uniform scaling (typically applied to fit input images to CNN input layers), and data augmentation, affect the performance of CNNs in automated microorganism classification. Methods: Using two well-established CNN architectures, AlexNet and DenseNet-121, both frequently applied in biomedical image analysis, we conducted a series of computational experiments on a standardized dataset of high-resolution bacterial images. Results: Our results demonstrate under which conditions these preprocessing strategies degrade or improve CNN performance. Using the findings from this research to optimize hyperparameters and train the CNNs, we achieved classification accuracies of 98.61% with AlexNet and 99.82% with DenseNet-121, surpassing the performance reported in current state-of-the-art studies. Conclusions: This study advances laboratory digitalization by reducing data preparation effort, training time, and computational costs, while improving the accuracy of microorganism classification with deep learning. Its contributions also benefit broader biomedical fields such as automated diagnostics, digital pathology, clinical decision support, and point-of-care imaging.

BioMedInformatics doi: 10.3390/biomedinformatics5040060

Authors: Sonia Lilia Mestizo-Gutiérrez Joan Arturo Jácome-Delgado Nicandro Cruz-Ramírez Alejandro Guerra-Hernández Jesús Alberto Torres-Sosa Viviana Yarel Rosales-Morales Gonzalo Emiliano Aranda-Abreu

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized primarily by motor impairments due to the loss of dopaminergic neurons. Despite extensive research, the precise causes of PD remain unknown, and reliable non-invasive biomarkers are still lacking. This study aimed to explore gene expression profiles in peripheral blood to identify potential biomarkers for PD using machine learning approaches. We analyzed microarray-based gene expression data from 105 individuals (50 PD patients, 33 with other neurodegenerative diseases, and 22 healthy controls) obtained from the GEO database (GSE6613). Preprocessing was performed using the “affy” package in R with Expresso normalization. Feature selection and classification were conducted using a decision tree approach (C4.5/J48 algorithm in WEKA), and model performance was evaluated with 10-fold cross-validation. Additional classifiers such as Support Vector Machine (SVM), the Naive Bayes classifier and Multilayer Perceptron Neural Network (MLP) were used for comparison. ROC curve analysis and Gene Ontology (GO) enrichment analysis were applied to the selected genes. A nine-gene decision tree model (TMEM104, TRIM33, GJB3, SPON2, SNAP25, TRAK2, SHPK, PIEZO1, RPL37) achieved 86.71% accuracy, 88% sensitivity, and 87% specificity. The model significantly outperformed other classifiers (SVM, Naive Bayes, MLP) in terms of overall predictive accuracy. ROC analysis showed moderate discrimination for some genes (e.g., TRAK2, TRIM33, PIEZO1), and GO enrichment revealed associations with synaptic processes, inflammation, mitochondrial transport, and stress response pathways. Our decision tree model based on blood gene expression profiles effectively discriminates between PD, other neurodegenerative conditions, and healthy controls, offering a non-invasive method for potential early diagnosis. Notably, TMEM104, TRIM33, and SNAP25 emerged as promising candidate biomarkers, warranting further investigation in larger and synthetic datasets to validate their clinical relevance.

BioMedInformatics doi: 10.3390/biomedinformatics5040059

Authors: Hasini Nakulugamuwa Gamage Jaskaran Gill Madhu Chetty Suryani Lim Jennifer Hallinan

Background: Reconstructing gene regulatory networks (GRNs) from gene expression data remains a major challenge in systems biology due to the inherent complexity of biological systems and the limitations of existing reconstruction methods, which often yield high false-positive rates. This study aims to develop a robust and adaptive approach to enhance the accuracy of inferred GRNs by integrating multiple modelling paradigms. Methods: We introduce EvoFuzzy, a novel algorithm that integrates evolutionary computation and fuzzy logic to aggregate GRNs reconstructed using Boolean, regression, and fuzzy modelling techniques. The algorithm initializes an equal number of individuals from each modelling method to form a diverse population, which evolves through fuzzy trigonometric differential evolution. Gene expression values are predicted using a fuzzy logic-based predictor with confidence levels, and a fitness function is applied to identify the optimal consensus network. Results: The proposed method was evaluated using simulated benchmark datasets and a real-world SOS gene repair dataset. Experimental results demonstrated that EvoFuzzy consistently outperformed existing state-of-the-art GRN reconstruction methods in terms of accuracy and robustness. Conclusions: The fuzzy trigonometric differential evolution approach plays a pivotal role in refining and aggregating multiple network outputs into a single, optimal consensus network, making EvoFuzzy a powerful and reliable framework for reconstructing biologically meaningful gene regulatory networks.

BioMedInformatics doi: 10.3390/biomedinformatics5040058

Authors: Lujin Abu Sabah Laura Ballisat Chiara De Sio Magdalena Dobrowolska Adam Chambers Jinyan Duan Susanna Guatelli Dousatsu Sakata Yuyao Shi Jaap Velthuis Anatoly Rosenfeld

Extensive research is being carried out on the application of α particles for cancer treatment. A key challenge in α therapy is how to deliver the α emitters to the tumour. In AlphaGlue, a novel treatment delivery concept, the α emitters are suspended in a thin layer of glue that is put on top of the tumour. In principle, this should be an easy and safe way to apply α therapy. In this study, the effectiveness of AlphaGlue is evaluated using GEANT4 and GEANT4-DNA simulations to calculate the DNA damage as a function of depth. Two radionuclides are considered in this work, 211At and 224Ra. The results indicate that, as a concept, the method offers a promising hypothesis for treating superficial tumours, such as skin cancer, when 224Ra is applied directly on the tissue and stabilized with a glue layer. This results in 2×10−5 complex double strand breaks and 5×10−5 double strand breaks at 5 mm depth per applied 224Ra atom. When applying a 224Ra atom concentration of (4.35±0.2)×1011/cm2 corresponding to an activity of (21.8±1)μCi/cm2 on the skin surface, the RBE weighted dose exceeds 20 Gy at 5 mm depth. Hence, there is significant cell death at 5 mm into the tissue; a depth matching clinical requirements for skin cancer treatment. Given the rapidly falling weighted dose versus depth curve, the treatment depth can be tuned with good precision. The results of this study show that AlphaGlue is a promosing treatment and open the pathway towards the next stage of the research, which includes in-vitro studies.

BioMedInformatics doi: 10.3390/biomedinformatics5040057

Authors: Krittin Naravejsakul Watcharaporn Cholamjiak Watcharapon Yajai Jakkaphong Inpun Waragunt Waratamrongpatai

Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is unclear. Methods: We analyzed 332 de-identified clinical records using six machine learning algorithms: Extra Trees, Gradient Boosting, XGBoost, Logistic Regression, Random Forest, and LightGBM. Two preprocessing strategies were applied: (i) removing rows with missing fasting blood sugar (FBs) and HbA1c, and (ii) dropping columns with Null FBs and HbA1c values. Model performance was evaluated using 10-fold cross-validation. Results: Serum vitamin D showed weak correlations with UTI subtypes but modest importance in tree-based models. The highest predictive accuracy was obtained with Extra Trees (0.9510) under the row-removal strategy and Random Forest (0.9525) under the column-dropping strategy. Models excluding vitamin D maintained comparable accuracy, suggesting minimal impact on overall predictive performance. Conclusions: Machine learning models demonstrated high accuracy and robustness in predicting UTI subtypes across preprocessing strategies. While vitamin D contributes as a supportive feature, it is not essential for reliable prediction. These findings highlight the adaptability and clinical utility of both vitamin D-inclusive and vitamin D-exclusive models, supporting deployment in diverse healthcare settings.

BioMedInformatics doi: 10.3390/biomedinformatics5040056

Authors: Sowad Rahman Md Azad Hossain Raju Abdullah Evna Jafar Muslima Akter Israt Jahan Suma Jia Uddin

Accurate spleen segmentation in abdominal CT scans remains a critical challenge in medical image analysis due to variable morphology, low tissue contrast, and proximity to similar anatomical structures. This paper presents an enhanced U-Net architecture that addresses these challenges through multi-slice contextual integration and interpretable deep learning. Our approach incorporates three-channel inputs from adjacent CT slices, implements a hybrid loss function combining Dice and binary cross-entropy terms, and integrates Grad-CAM visualization for enhanced model interpretability. Comprehensive evaluation on the Medical Decathlon dataset demonstrates superior performance, with a Dice similarity coefficient of 0.923 ± 0.04, outperforming standard 2D approaches by 3.2%. The model exhibits robust performance across varying slice thicknesses, contrast phases, and pathological conditions. Grad-CAM analysis reveals focused attention on spleen–tissue interfaces and internal vascular structures, providing clinical insight into model decision-making. The system demonstrates practical applicability for automated splenic volumetry, trauma assessment, and surgical planning, with processing times suitable for clinical workflow integration.

BioMedInformatics doi: 10.3390/biomedinformatics5030055

Authors: Uraquitan Lima Filho Tiago Alexandre Pais Ricardo Jorge Pais

Background: The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deployment becomes challenging. Methods: We developed a novel composite framework that integrates visual inspection of Model Scoring Distribution Analysis (MSDA) with a new scoring metric (MSDscore). The methodology was implemented within the Digital Phenomics platform as the MSDanalyser tool and tested by generating and evaluating 27 predictive models developed for breast, lung, and renal cancer prognosis. Results: Our approach enabled a detailed inspection of true-positive, false-positive, true-negative, and false-negative distributions across the scoring space, capturing local performance patterns overlooked by conventional metrics. In contrast with the minimal variation between models obtained by global metrics, the MSDA methodology revealed substantial differences in score region behaviour, allowing better discrimination between models. Conclusions: Integrating our composite framework alongside traditional performance metrics provides a complementary and more nuanced approach to model selection in clinical and biomedical settings.

BioMedInformatics doi: 10.3390/biomedinformatics5030054

Authors: Kaloso M. Tlotleng Rodrigo S. Jamisola Jeniffer L. Brown

This paper investigates the state of substance use disorder (SUD) and the frequency of substance use by utilizing three unsupervised machine learning techniques, based on the Diagnostic and Statistical Manual 5 (DSM-5) of mental health disorders. We used data obtained from the National Survey on Drug Use and Health (NSDUH) 2019 database with random participants who had undergone clinical assessments by mental health professionals and whose clinical diagnoses were not known. This approach classifies SUD status by discovering patterns or correlations from the trained model. Our results were analyzed and validated by a mental health professional. The three unsupervised machine learning techniques that we used comprised k-means clustering, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN), which were applied to alcohol, marijuana, and cocaine datasets. The clustering results were validated using the silhouette score and the 95% confidence interval (CI). The results from this study may be used to supplement psychiatric evaluations.

BioMedInformatics doi: 10.3390/biomedinformatics5030053

Authors: Mamoun Qjidaa Anass Benfares Mohammed Amine El Azami El Hassani Amine Benkabbou Amine Souadka Anass Majbar Zakaria El Moatassim Maroua Oumlaz Oumayma Lahnaoui Raouf Mouhcine Ahmed Lakhssassi Abdeljabbar Cherkaoui

Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due to the reduction of individual errors. Flexibility is also a key asset, with combination methods such as majority voting or weighted averaging. The model enables managing the uncertainty associated with individual decisions to obtain a more reliable final decision. The combination of decisions improves the overall accuracy of the system. Methods: This research introduces a new deep learning-based architecture for automatically segmenting hepatic vessels and tumors from CT scans, utilizing stacking, decision fusion, and deep transfer learning to achieve high-accuracy and rapid segmentation. This study employed two distinct datasets: the external “Medical Segmentation Decathlon (MSD) task 08” dataset and an internal dataset procured from Ibn Sina University Hospital encompassing a cohort of 112 patients with chronic liver disease who underwent contrast-enhanced abdominal CT scans. Results: The proposed segmentation model reached a DSC of 83.21 and an IoU of 72.76 for hepatic vasculature and tumor segmentation, thereby exceeding the performance benchmarks established by the majority of antecedent studies. Conclusions: This study introduces an automated method for liver vessels and liver tumor segmentation, combining precision and stability to bridge the clinical gap. Furthermore, decision fusion-based stacking models have a significant impact on clinical applications by enhancing diagnostic accuracy, enabling personalized care through the integration of genetic, environmental, and clinical data, optimizing clinical trials, and facilitating the development of personalized medicines and therapies.

BioMedInformatics doi: 10.3390/biomedinformatics5030052

Authors: Amr M. Ali Orkun Baloglu

Background: Pediatric Intensive Care Unit (PICU) outcome prediction is challenging, and machine learning (ML) can enhance it by leveraging large datasets. Methods: We built an ML model to predict PICU outcomes (“Death vs. Survival”, “Death or Morbidity vs. Survival without morbidity”, and “New Morbidity vs. Survival without new morbidity”) using the Trichotomous Outcome Prediction in Critical Care (TOPICC) study dataset. The model used the Light Gradient-Boosting Machine (LightGBM) algorithm, which was trained on 85% of the dataset and tested on 15% utilizing 10-fold cross validation. Results: The model demonstrated high accuracy across all dichotomies, with 0.98 for “Death vs. Survival”, 0.92 for “Death or New Morbidity vs. Survival without New Morbidity”, and 0.93 for “New Morbidity vs. Survival without New Morbidity.” The AUC-ROC values were also strong, at 0.89, 0.79, and 0.74, respectively. The precision was highest for “Death vs. Survival” (0.92), followed by 0.45 and 0.30 for the other dichotomies. The recalls were low, at 0.26, 0.31, and 0.34, reflecting the model’s difficulty in identifying all positive cases. The AUC-PR values (0.43, 0.37, and 0.20) highlight this trade-off. Conclusions: The LightGBM model demonstrated a predictive performance comparable to previously reported logistic regression models in predicting PICU outcomes. Future work should focus on enhancing the model’s performance and further validation across larger datasets to assess the model’s generalizability and real-world applicability.

BioMedInformatics doi: 10.3390/biomedinformatics5030051

Authors: Nabil Marzoug Khidhr Halab Othmane El Meslouhi Zouhair Elamrani Abou Elassad Moulay A. Akhloufi

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging.

BioMedInformatics doi: 10.3390/biomedinformatics5030050

Authors: Sai Sangameswara Aadithya Kanduri Apoorv Prasad Susan McRoy

Background: Virtual coaching can help people adopt new healthful behaviors by encouraging them to set specific goals and helping them review their progress. One challenge in creating such systems is analyzing clients’ statements about their activities. Limiting people to selecting among predefined answers detracts from the naturalness of conversations and user engagement. Large Language Models (LLMs) offer the promise of covering a wide range of expressions. However, using an LLM for simple entity extraction would not necessarily perform better than functions coded in a programming language, while creating higher long-term costs. Methods: This study uses a real data set of annotated human coaching dialogs to develop LLM-based models for two training scenarios: one that generates pattern-matching functions and the other which does direct extraction. We use models of different sizes and complexity, including Meta-Llama, Gemma, and ChatGPT, and calculate their speed and accuracy. Results: LLM-generated pattern-matching functions took an average of 10 milliseconds (ms) per item as compared to 900 ms. (ChatGPT 3.5 Turbo) to 5 s (Llama 2 70B). The accuracy for pattern matching was 99% on real data, while LLM accuracy ranged from 90% (Llama 2 70B) to 100% (ChatGPT 3.5 Turbo), on both real and synthetically generated examples created for fine-tuning. Conclusions: These findings suggest promising directions for future research that combines both methods (reserving the LLM for cases that cannot be matched directly) or that use LLMs to generate synthetic training data with more expressive variety which can be used to improve the coverage of either generated codes or fine-tuned models.

BioMedInformatics doi: 10.3390/biomedinformatics5030049

Authors: Fareed Ud Din Nabaraj Giri Namrata Shetty Tom Hilton Niusha Shafiabady Phillip J. Tully

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions.

BioMedInformatics doi: 10.3390/biomedinformatics5030048

Authors: Marie-Jeanne Fradette Julie Azrak Florence Cousineau Marie Désilets Alexandre Dumais

Virtual-reality exposure therapy (VRET) is an emerging treatment for psychiatric disorders that enables immersive and controlled exposure to anxiety-provoking stimuli. Recent developments integrate real-time physiological monitoring, including heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG), to dynamically tailor therapeutic interventions. This systematic review examines studies that combine VRET with physiological data to adapt virtual environments in real time. A comprehensive search of major databases identified fifteen studies meeting the inclusion criteria: all employed physiological monitoring and adaptive features, with ten using biofeedback to modulate exposure based on single or multimodal physiological measures. The remaining studies leveraged physiological signals to inform scenario selection or threat modulation using dynamic categorization algorithms and machine learning. Although findings currently show an overrepresentation of anxiety disorders, recent studies are increasingly involving more diverse clinical populations. Results suggest that adaptive VRET is technically feasible and offers promising personalization benefits; however, the limited number of studies, methodological variability, and small sample sizes constrain broader conclusions. Future research should prioritize rigorous experimental designs, standardized outcome measures, and greater diversity in clinical populations. Adaptive VRET represents a frontier in precision psychiatry, where real-time biosensing and immersive technologies converge to enhance individualized mental health care.

BioMedInformatics doi: 10.3390/biomedinformatics5030047

Authors: Reetesh Kumar Subhomoi Borkotoky Rohan Gupta Jyoti Gupta Somnath Maji Savitri Tiwari Rajeev K. Tyagi Baldo Oliva

Background: Human Metapneumovirus (HMPV) is a respiratory virus in the Pneumoviridae family. HMPV is an enveloped, negative-sense RNA virus encoding three surface proteins: SH, G, and F. The highly immunogenic fusion (F) protein is essential for viral entry and a key target for vaccine development. The F protein exists in two conformations: prefusion and postfusion. The prefusion form is highly immunogenic and considered a potent vaccine antigen. However, this conformation needs to be stabilized to improve its immunogenicity for effective vaccine development. Specific mutations are necessary to maintain the prefusion state and prevent it from changing to the postfusion form. Methods: In silico mutagenesis was performed on the C-terminal domain of the pre-F protein, focusing on five amino acids at positions 469 to 473 (LVDQS), using the established pre-F structure (PDB: 8W3Q) as the reference. The amino acid sequence was sequentially mutated based on hydrophobicity, resulting in mutants M1 (IIFLL), M2 (LLIVL), M3 (WWVLL), and M4 (YMWLL). Increasing hydrophobicity was found to enhance protein stability and structural rigidity. Results: Epitope mapping revealed that all mutants displayed significant B and T cell epitopes similar to the reference protein. The structure and stability of all mutants were analyzed using molecular dynamics simulations, free energy calculations, and secondary structure analysis. Based on the lowest RMSD, clash score, MolProbity value, stable radius of gyration, and low RMSF, the M1 mutant demonstrated superior structural stability. Conclusions: Our findings indicate that the M1 mutant of the pre-F protein could be the most stable and structurally accurate candidate for vaccine development against HMPV.

BioMedInformatics doi: 10.3390/biomedinformatics5030046

Authors: Md. Atiqur Rahman M. Saddam Hossain Khan Yutaka Watanobe Jarin Tasnim Prioty Tasfia Tahsin Annita Samura Rahman Md. Shakil Hossain Saddit Ahmed Aitijjo Rafsun Islam Taskin Victor Dhrubo Abubokor Hanip Touhid Bhuiyan

Breast cancer remains a critical global health challenge, with over 2.1 million new cases annually. This review systematically evaluates recent advancements (2022–2024) in machine and deep learning approaches for breast cancer detection and risk management. Our analysis demonstrates that deep learning models achieve 90–99% accuracy across imaging modalities, with convolutional neural networks showing particular promise in mammography (99.96% accuracy) and ultrasound (100% accuracy) applications. Tabular data models using XGBoost achieve comparable performance (99.12% accuracy) for risk prediction. The study confirms that lifestyle modifications (dietary changes, BMI management, and alcohol reduction) significantly mitigate breast cancer risk. Key findings include the following: (1) hybrid models combining imaging and clinical data enhance early detection, (2) thermal imaging achieves high diagnostic accuracy (97–100% in optimized models) while offering a cost-effective, less hazardous screening option, (3) challenges persist in data variability and model interpretability. These results highlight the need for integrated diagnostic systems combining technological innovations with preventive strategies. The review underscores AI’s transformative potential in breast cancer diagnosis while emphasizing the continued importance of risk factor management. Future research should prioritize multi-modal data integration and clinically interpretable models.

BioMedInformatics doi: 10.3390/biomedinformatics5030045

Authors: Christos Tselios Ioannis Vezakis Apostolos Zaravinos George I. Lambrou

Background: The amount of data produced from biological experiments has increased geometrically, posing a challenge for the development of new methodologies that could enable their interpretation. We propose a novel approach for the analysis of transcriptomic data derived from acute lymphoblastic leukemia (ALL) and rhabdomyosarcoma (RMS) cell lines, using bioinformatics, systems biology and geometrical approaches. Methods: The expression profile of each cell line was investigated using microarrays, and identified genes were used to create a systems pathway model, which was then simulated using differential equations. The transcriptomic profile used involved genes with similar expression levels. The simulated results were further analyzed using geometrical approaches to identify common expressional dynamics. Results: We simulated and analyzed the system network using time series, regression analysis and helical functions, detecting predictable structures after iterating the modelled biological network, focusing on TIE1, STAT1, MAPK14 and ADAM17. Our results show that such common attributes in gene expression patterns can lead to more effective treatment options and help in the discovery of universal tumor biomarkers. Discussion: Our approach was able to identify complex structures in gene expression patterns, indicating that such approaches could prove useful towards the understanding of the complex tumor dynamics.

BioMedInformatics doi: 10.3390/biomedinformatics5030044

Authors: Marko Mihajlovic Marina Marjanovic

Accurate segmentation of kidney microstructures in whole slide images (WSIs) is essential for the diagnosis and monitoring of renal diseases. In this study, an end-to-end instance segmentation pipeline was developed for the detection of glomeruli and blood vessels in hematoxylin and eosin (H&E) stained kidney tissue. A tiling-based strategy was employed using Slicing Aided Hyper Inference (SAHI) to manage the resolution and scale of WSIs and the performance of two segmentation models, YOLOv11 and YOLOv12, was comparatively evaluated. The influence of tile overlap ratios on segmentation quality and inference efficiency was assessed, with configurations identified that balance object continuity and computational cost. To address object fragmentation at tile boundaries, an Enhanced Syncretic Mask Merging algorithm was introduced, incorporating morphological and spatial constraints. The algorithm’s hyperparameters were optimized using Particle Swarm Optimization (PSO), with vessel and glomerulus-specific performance targets. The optimization process revealed key parameters affecting segmentation quality, particularly for vessel structures with fine, elongated morphology. When compared with a baseline without postprocessing, improvements in segmentation precision were observed, notably a 48% average increase for glomeruli and up to 17% for blood vessels. The proposed framework demonstrates a balance between accuracy and efficiency, supporting scalable histopathology analysis and contributing to the Vasculature Common Coordinate Framework (VCCF) and Human Reference Atlas (HRA).

BioMedInformatics doi: 10.3390/biomedinformatics5030043

Authors: Md. Rifat Aknda Fahmid Al Farid Jia Uddin Sarina Mansor Muhammad Golam Kibria

Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning models have been developed, they demonstrate significant limitations. An interpretable and improved transfer learning model for binary skin cancer classification is proposed in this research, which uses the last convolutional block of VGG16 as the feature extractor. The methodology focuses on addressing the existing limitations in skin cancer classification, to support dermatologists and potentially saving lives through advanced, reliable, and accessible AI-driven diagnostic tools. Explainable AI is incorporated for the visualization and explanation of classifications. Multiple optimization techniques are applied to avoid overfitting, ensure stable training, and enhance the classification accuracy of dermoscopic images into benign and malignant classes. The proposed model shows 90.91% classification accuracy, which is better than state-of-the-art models and established approaches in skin cancer classification. An interactive desktop application integrating the model is developed, enabling real-time preliminary screening with offline access.

BioMedInformatics doi: 10.3390/biomedinformatics5030042

Authors: Chunyang Li Julia Bohman Vikas Patil Richard Mcshinsky Christina Yong Zach Burningham Matthew Samore Ahmad S. Halwani

Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized consideration for appropriate treatment. However, there is no consensus regarding when to switch from androgen deprivation therapy (ADT) to more aggressive treatments like abiraterone or enzalutamide. Methods: We analyzed 5037 nmCRPC patients and employed a Weibull Time to Event Recurrent Neural Network to identify patients who would benefit from switching from ADT to abiraterone/enzalutamide. We evaluated this model using differential treatment benefits measured by the Kaplan–Meier estimation and milestone probabilities. Results: The model achieved an area under the curve of 0.738 (standard deviation (SD): 0.057) for patients treated with abiraterone/enzalutamide and 0.693 (SD: 0.02) for patients exclusively treated with ADT at the 2-year milestone. The model recommended 14% of ADT patients switch to abiraterone/enzalutamide. Analysis showed a statistically significant absolute improvement using model-recommended treatments in progression-free survival (PFS) of 0.24 (95% confidence interval (CI): 0.23–0.24) at the 2-year milestone (PFS rate increasing from 0.50 to 0.74) with a hazard ratio of 0.44 (95% CI: 0.39–0.50). Conclusions: Our model successfully identified nmCRPC patients who would benefit from switching to abiraterone/enzalutamide, demonstrating potential outcome improvements.

BioMedInformatics doi: 10.3390/biomedinformatics5030041

Authors: Maryem Zahid Mohammed Rziza Rachid Alaoui

This paper proposes a hybrid method for skin lesion classification combining deep learning features with conventional descriptors such as HOG, Gabor, SIFT, and LBP. Feature extraction was performed by extracting features of interest within the tumor area using suggested fusion methods. We tested and compared features obtained from different deep learning models coupled to HOG-based features. Dimensionality reduction and performance improvement were achieved by Principal Component Analysis, after which SVM was used for classification. The compared methods were tested on the reference database skin cancer-malignant-vs-benign. The results show a significant improvement in terms of accuracy due to complementarity between the conventional and deep learning-based methods. Specifically, the addition of HOG descriptors led to an accuracy increase of 5% for EfficientNetB0, 7% for ResNet50, 5% for ResNet101, 1% for NASNetMobile, 1% for DenseNet201, and 1% for MobileNetV2. These findings confirm that feature fusion significantly enhances performance compared to the individual application of each method.

BioMedInformatics doi: 10.3390/biomedinformatics5030040

Authors: Apoorv Prasad Setayesh Abiazi Shalmani Lu He Yang Wang Susan McRoy

Background: Social media represents a unique opportunity to investigate the perspectives of people with eating disorders at scale. One forum alone, r/EatingDisorders, now has 113,000 members worldwide. In less than a day, where a manual analysis might sample a few dozen items, automatic classification using large language models (LLMs) can analyze thousands of posts. Methods: Here, we compare multiple strategies for invoking an LLM, including ones that include examples (few-shot) and annotation guidelines, to classify eating disorder content across 14 predefined themes using Llama3.1:8b on 6850 social media posts. In addition to standard metrics, we calculate four novel dimensions of classification quality: a Category Divergence Index, confidence scores (overall model certainty), focus scores (a measure of decisiveness for selected subsets of themes), and dominance scores (primary theme identification strength). Results: By every measure, invoking an LLM without extensive guidance and examples (zero-shot) is insufficient. Zero-shot had worse mean category divergence (7.17 versus 3.17). Whereas, few-shot yielded higher mean confidence, 0.42 versus 0.27, and higher mean dominance, 0.81 versus 0.46. Overall, a few-shot approach improved quality measures across nearly 90% of predictions. Conclusions: These findings suggest that LLMs, if invoked with expert instructions and helpful examples, can provide instantaneous high-quality annotation, enabling automated mental health content moderation systems or future clinical research.

BioMedInformatics doi: 10.3390/biomedinformatics5030039

Authors: Natalia Amasiadi Maria Aslani-Gkotzamanidou Leonidas Theodorakopoulos Alexandra Theodoropoulou George A. Krimpas Christos Merkouris Aristeidis Karras

Lung-cancer incidence is projected to rise by 50% by 2035, underscoring the need for accurate yet accessible risk-stratification tools. We trained a Bayesian neural network on 300 annotated chest-CT scans from the public LIDC–IDRI cohort, integrating clinical metadata. Hamiltonian Monte-Carlo sampling (10 000 posterior draws) captured parameter uncertainty; performance was assessed with stratified five-fold cross-validation and on three independent multi-centre cohorts. On the locked internal test set, the model achieved 99.0% accuracy, AUC = 0.990 and macro-F1 = 0.987. External validation across 824 scans yielded a mean AUC of 0.933 and an expected calibration error <0.034, while eliminating false positives for benign nodules and providing voxel-level uncertainty maps. Uncertainty-aware Bayesian deep learning delivers state-of-the-art, well-calibrated lung-cancer risk predictions from a single CT scan, supporting personalised screening intervals and safe deployment in clinical workflows.

BioMedInformatics doi: 10.3390/biomedinformatics5030038

Authors: Youssef Errafik Younes Dhassi Mohamed Baghrous Adil Kenzi

Technological advancements and AI-based research have significantly influenced our daily lives. Human activity recognition (HAR) is a key area at the intersection of various AI technologies and application domains. In this study, we present our novel time series classification approach for monitoring the physical behaviors of the elderly and patients. This approach, which integrates supervised and unsupervised methods with generative models, has been validated for HAR, showing promising results. Our method was specifically adapted for healthcare and surveillance applications, enhancing the classification of physical behaviors in the elderly. The hybrid approach proved its effectiveness on the HAR70+ dataset, surpassing traditional recurrent convolutional network-based approaches. We further evaluated the surveillance system for the elderly (Surv-Sys-Elderly) model on the HARTH and HAR70+ datasets, achieving an accuracy of 94,3% on the HAR70+ dataset for recognizing elderly behaviors, highlighting its robustness and suitability for both clinical and domestic environments.

BioMedInformatics doi: 10.3390/biomedinformatics5030037

Authors: Syed Arman Rabbani Mohamed El-Tanani Shrestha Sharma Syed Salman Rabbani Yahia El-Tanani Rakesh Kumar Manita Saini

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems.

BioMedInformatics doi: 10.3390/biomedinformatics5030036

Authors: Zvinodashe Revesai Okuthe P. Kogeda

Food pattern recognition plays a crucial role in modern healthcare by enabling automated dietary monitoring and personalised nutritional interventions, particularly for vulnerable populations with complex dietary needs. Current food recognition systems struggle to balance high accuracy with interpretability and computational efficiency when analysing complex meal compositions in real-world settings. We developed a novel self-explaining neural architecture that integrates specialised attention mechanisms with temporal modules within a streamlined framework. Our methodology employs hierarchical feature extraction through successive convolution operations, multi-head attention mechanisms for pattern classification, and bidirectional LSTM networks for temporal analysis. Architecture incorporates self-explaining components utilising attention-based mechanisms and interpretable concept encoders to maintain transparency. We evaluated our model on the FOOD101 dataset using 5-fold cross-validation, ablation studies, and comprehensive computational efficiency assessments. Training employed multi-objective optimisation with adaptive learning rates and specialised loss functions designed for dietary pattern recognition. Experiments demonstrate our model’s superior performance, achieving 94.1% accuracy with only 29.3 ms inference latency and 3.8 GB memory usage, representing a 63.3% parameter reduction compared to baseline transformers. The system maintains detection rates above 84% in complex multi-item recognition scenarios, whilst feature attribution analysis achieved scores of 0.89 for primary components. Cross-validation confirmed consistent performance with accuracy ranging from 92.8% to 93.5% across all folds. This research advances automated dietary analysis by providing an efficient, interpretable solution for food recognition with direct applications in nutritional monitoring and personalised healthcare, particularly benefiting vulnerable populations who require transparent and trustworthy dietary guidance.

BioMedInformatics doi: 10.3390/biomedinformatics5030035

Authors: Amira S. Awaad Yomna M. Elbarawy H. Mancy Naglaa E. Ghannam

Background: Anemia, a common health disorder affecting populations globally, demands timely and accurate diagnosis for treatment to be effective. The aim of this paper is to detect and classify four types of anemia: hgb, iron-deficiency, folate-deficiency, and B12-deficiency anemia. Methods: This paper proposes an ontology-enhanced machine learning (ML) framework to classify types of anemia from CBC data obtained from Kaggle, which contains 15,300 patient records. It evaluates the effects of classical versus deep classifiers on imbalanced and oversampled training samples. Tests include KNN, SVM, DT, RF, CNN, CNN+SVM, CNN+RF, and XGBoost. Another interesting contribution is the use of ontological reasoning via SPARQL queries to semantically enrich clinical features with categories like “Low Hemoglobin” or “Macrocytic MCV”. These semantic features were then used in both classical (SVM) and deep hybrid models (CNN+SVM). Results: Ontology-enhanced and CNN hybrid models perform competitively when paired with ROS or ADASYN, but their performance degrades significantly on the original dataset. There were tremendous performance gains with ontology-enhanced models in that Onto-CNN+SVM achieved an F1-score (1.00) for all the four types of anemia under ROS sampling, while Onto-SVM exhibited more than 20% improvement in F1-scores for minority categories like folate and B12 when compared to baseline models, except XGBoost. Conclusions: Ontology-driven knowledge coalescence has been shown to improve classification results; however, XGBoost consistently outperformed all other classifiers across all data conditions, making it the most robust and reliable model for clinically relevant decision-support systems in anemia diagnosis.

BioMedInformatics doi: 10.3390/biomedinformatics5030034

Authors: Mohammad Najeh Samara Kimberly D. Harry

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making.

BioMedInformatics doi: 10.3390/biomedinformatics5030033

Authors: Ebtesam Alomari

Background: Chronic diseases significantly burden healthcare systems due to the need for long-term treatment. Early diagnosis is critical for effective management and minimizing risk. The current traditional diagnostic approaches face various challenges regarding efficiency and cost. Digitized healthcare demonstrates several opportunities for reducing human errors, increasing clinical outcomes, tracing data, etc. Artificial Intelligence (AI) has emerged as a transformative tool in healthcare. Subsequently, the evolution of Generative AI represents a new wave. Large Language Models (LLMs), such as ChatGPT, are promising tools for enhancing diagnostic processes, but their potential in this domain remains underexplored. Methods: This study represents the first systematic evaluation of ChatGPT’s performance in chronic disease prediction, specifically targeting heart disease and diabetes. This study compares the effectiveness of zero-shot, few-shot, and CoT reasoning with feature selection techniques and prompt formulations in disease prediction tasks. The two latest versions of GPT4 (GPT-4o and GPT-4o-mini) are tested. Then, the results are evaluated against the best models from the literature. Results: The results indicate that GPT-4o significantly beat GPT-4o-mini in all scenarios regarding accuracy, precision, and F1-score. Moreover, a 5-shot learning strategy demonstrates superior performance to zero-shot, few-shot (3-shot and 10-shot), and various CoT reasoning strategies. The 5-shot learning strategy with GPT-4o achieved an accuracy of 77.07% in diabetes prediction using the Pima Indian Diabetes Dataset, 75.85% using the Frankfurt Hospital Diabetes Dataset, and 83.65% in heart disease prediction. Subsequently, refining prompt formulations resulted in notable improvements, particularly for the heart dataset (5% performance increase using GPT-4o), emphasizing the importance of prompt engineering. Conclusions: Even though ChatGPT does not outperform traditional machine learning and deep learning models, the findings highlight its potential as a complementary tool in disease prediction. Additionally, this work provides value by setting a clear performance baseline for future work on these tasks

BioMedInformatics doi: 10.3390/biomedinformatics5020032

Authors: Ferid Ben Ali Denis Mustafov Maria Braoudaki Sola Adeleke Iosif Mporas

Background: Lung adenocarcinoma is one of the major subtype of non-Small Cell Lung Cancer and biomarkers are essential to be identified for early diagnosis. The study aims to find in silico and preliminary in vitro analysis of potential biomarkers for lung adenocarcinoma. Methods: Bioinformatics analysis in parallel to data mining analysis was performed on microarray data with lung adenocarcinoma samples to identify potent gene biomarkers associated with lung cancer type. Afterwards, these genes were then validated in vitro using RT-qPCR analysis in cancerous (Calu-3) and non-cancerous (MRC-5) cell lines. Moreover, these genes were used in machine learning-based analysis to classify lung adenocarcinoma samples from controls. The analysis includes three experiments—the bioinformatic (in silico), in vitro, and machine learning analyses. Results: The three experiments identified four genes, namely, SLC15A1, GPR123 (ADGRA1), KCNAB2, and KNDC1, as key biomarkers and the most relevant gene features for distinguishing lung adenocarcinoma from control. Conclusions: This study identifies four biomarkers associated with lung adenocarcinoma through bioinformatics, in vitro and machine learning analyses. These four genes shows strong potential for further investigation in clinical research.

BioMedInformatics doi: 10.3390/biomedinformatics5020031

Authors: Nicki Lentz-Nielsen Lars Maaløe Pascal Madeleine Stig Nikolaj Blomberg

Background: Chronic obstructive pulmonary disease (COPD) is projected to be the third-leading cause of death by 2030. Traditional spirometry for the monitoring of the forced expiratory volume in one second (FEV1) can provoke discomfort and anxiety. This study aimed to validate AI models using daily audio recordings as an alternative for FEV1 estimation in home settings. Methods: Twenty-three participants with moderate to severe COPD recorded daily audio readings of standardized texts and measured their FEV1 using spirometry over nine months. Participants also recorded biomarkers (heart rate, temperature, oxygen saturation) via tablet application. Various machine learning models were trained using acoustic features extracted from 2053 recordings, with K-nearest neighbor, random forest, XGBoost, and linear models evaluated using 10-fold cross-validation. Results: The K-nearest neighbors model achieved a root mean square error of 174 mL/s on the validation data. The limit of agreement (LoA) ranged from −333.21 to 347.26 mL/s. Despite an error range of −1252 to 1435 mL/s, most predictions fell within the LoA, indicating good performance in estimating the FEV1. Conclusions: The predictive model showed promising results, with a narrower LoA compared to traditional unsupervised spirometry methods. The AI models effectively used audio to predict the FEV1, suggesting a viable non-invasive approach for COPD monitoring that could enhance patient comfort and accessibility in home settings.

BioMedInformatics doi: 10.3390/biomedinformatics5020030

Authors: Md Atiqur Rahman Mohammad Badrul Alam Miah Md. Abir Hossain A. S. M. Sanwar Hosen

Background: Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. Brain tumors can be broadly classified into two categories: primary tumors, which originate within the brain, and secondary tumors, which are metastatic in nature. Effective glioma, meningioma, and pituitary tumor diagnosis and treatment requires the precise differentiation of these tumors as well as non-tumors for improved clinical outcomes. Methods: Here, we present a new method to classify brain tumors based on the MobileNetV2 architecture with advanced preprocessing for high accuracy. We accessed an MRI image dataset from Kaggle that contained 1311 images in the test set. We split the data into 80% training and 20% testing. All images underwent extensive preprocessing, including grayscale conversion, noise removal, and contrast-limited-adaptive-histogram equalization (CLAHE). All images were resized to 224 × 224 pixels. Using transfer learning, the baseline frozen layers were kept intact while the top layers were trained with a learning rate of 0.0001, which was tuned to the model’s requirements using early stopping to avoid overfitting. Results: With the outlined methodology, we obtained an astounding accuracy of 99.16%, including strong performance in the no-tumor category, where recall rates were approaching 100% and false positive rates were minimized. Conclusions: These findings strongly indicate that the application of lightweight convolutional neural networks in diagnostic imaging can considerably expedite accurate brain tumor identification by radiologists.

BioMedInformatics doi: 10.3390/biomedinformatics5020029

Authors: Erica K. Ventura-García Mónica A. Valdez-Solana Claudia Avitia-Domínguez Guadalupe García-Arenas Alfredo Téllez-Valencia Nagamani Balagurusamy Erick Sierra-Campos

Background: Pleurotus salmoneostramineus is acknowledged as a reliable source of high-quality protein, with its protein concentrates, hydrolysates, and peptides potentially offering health benefits to humans. However, studies validating the medicinal effects of P. salmoneostramineus proteins, particularly the pink chromoprotein, are currently absent. Methods: This study explores anticancer peptides from the chromoprotein of P. salmoneostramineus, evaluating their ability to bind UCP2 via in silico analysis. Additionally, it assesses the protein hydrolysate from P. salmoneostramineus (PSPs) effect on HepG2 cell proliferation and mitochondrial metabolism, focusing on uncoupling protein activity. Results: Eight peptides were identified as potential UCP2 inhibitors. According to mACPpred2.0 and CSM-peptides servers, the peptides TSMQSSL, QEGQKL, SEDSGEA, and GRNSL exhibit promising anticancer properties. These anticancer peptides yielded the following docking scores (kcal/mol) when tested against UCP2: TSMQSSL (−166.75), QEGQKL (−126.06), SEDSGEA (−99.93), and GRNSL (−137.93). Molecular dynamics simulations have shown that the peptides establish stable interactions with UCP2 through salt bridges, hydrophobic interactions, and hydrogen bonds, implying that hydrogen bonding with RRR88 and FVW92 causes conformational changes in UCP2. Moreover, the outcomes of this study indicated that PSPs possess an antiproliferative effect on HepG2 cells and lower mitochondrial bioenergetics, especially UCP2 activity. Conclusions: These findings suggest that peptides from P.salmoneostramineus can inhibit UCP2, offering a promising approach for cancer prevention, playing therapeutic roles in treatment, and providing a basis for designing peptide-based cancer therapies.

BioMedInformatics doi: 10.3390/biomedinformatics5020028

Authors: Maria Ganopoulou Konstantinos Fokianos Christos Bakirtzis Lefteris Angelis Theodoros Moysiadis

Background: Health-related quality of life (HRQoL) questionnaires are essential for understanding the physical, psychological, lifestyle, and social factors that impact patients’ well-being. Causal discovery demonstrates significant potential in this direction; however, it has not yet been thoroughly assessed. This study aimed to explore the perspective of utilizing causal discovery as a methodological tool for binary classification of patients based on HRQoL questionnaire data. Methods: The focus was on questionnaire structures similar to the EQ-5D-5L, which includes both ordinal and quantitative items. A customized classification algorithm is proposed, which utilizes the differences between the causal structures derived from the HRQoL questionnaire answers of patients who belong to two distinct groups. This algorithm was evaluated using the correct classification rate (CCR) and the misclassification rate (MR) based on simulated data under conditions of varying sample size and causal structures’ complexity, and within a real-world data application. Results: In both the simulation and application, the CCR exhibited larger values compared to the MR; however, the percentages that the algorithm could not result in a decision were, in general, not negligible. The adjusted CCR (algorithm yields a decision) exhibited substantially improved values compared to the CCR in both analyses. Within the application, the algorithm showed mixed performance compared to a standard stepwise binary logistic regression approach. Conclusions: The proposed algorithm has the potential to correctly classify patients, but further investigation is needed to evaluate its performance under different scenarios in a large-scale real-world setting. Determining the necessary conditions for successful classification would result in effectively exploiting causal discovery to further advance the role of HRQoL questionnaires in patient care and management.

BioMedInformatics doi: 10.3390/biomedinformatics5020027

Authors: Isaure Quétel Sourakhata Tirera Damien Cazenave Nina Allouch Chloé Baum Yann Reynaud Degrâce Batantou Mabandza Virginie Nerrière Serge Vedy Matthieu Pot Sébastien Breurec Anne Lavergne Séverine Ferdinand Vincent Guerlais David Couvin

Background: Bioinformatics is increasingly used in various scientific works. Large amounts of heterogeneous data are being generated these days. It is difficult to interpret and analyze these data effectively. Several software tools have been developed to facilitate the handling and analysis of biological data, based on specific needs. Methods: The Galaxy web platform is one of these software tools, allowing free access to users and facilitating the use of thousands of tools. Other software tools, such as Bioconda or Jupyter Notebook, facilitate the installation of tools and their dependencies. In addition to these tools, RStudio can be mentioned as a powerful interface that facilitates the use of the R programming language for data analysis and statistics. Results: The aim of this study is to provide the scientific community with guides on how to perform bioinformatics/biostatistical analyses in a simpler manner. With this work, we also try to democratize well-documented software tools to make them suitable for both bioinformaticians and non-bioinformaticians. We believe that user-friendly guides and real-life/concrete examples will provide end-users with suitable and easy-to-use methods for their bioinformatics analysis needs. Furthermore, tutorials and usage examples are available on our dedicated GitHub repository. Conclusions: These tutorials/examples (In English and/or French) could be used as pedagogical tools to promote bioinformatics analysis and offer potential solutions to several bioinformatics needs. Special emphasis is placed on microbial omics data analysis.

BioMedInformatics doi: 10.3390/biomedinformatics5020026

Authors: Jesús Alberto Torres-Sosa Gonzalo Emiliano Aranda-Abreu Nicandro Cruz-Ramírez Sonia Lilia Mestizo-Gutiérrez

COVID-19 has caused millions of deaths around the world. The respiratory system is the main target of this disease, but it has also been reported to attack the central nervous system, creating a neuroinflammatory environment with the release of proinflammatory cytokines. There are several studies suggesting a possible relationship between Alzheimer’s disease and COVID-19. Therefore, in this study, machine learning microarray analysis was performed to identify key genes in COVID-19 that may be associated with Alzheimer’s disease. The dataset is identified as GSE177477, containing 47 samples. A bioconductor oligo package in the RStudio (version 4.3.3) environment was used to process and normalize the data. Subsequently, one-way ANOVA was used to obtain differentially expressed genes. We used decision tree generation to classify 47 samples. The study identified 1856 differentially expressed genes. Three decision trees were generated where three genes (DNAJC16, TREM1, and UCP2) were identified that differentiated patients. The best decision tree obtained an accuracy of 72.34%, with a sensitivity of 72.34% and a specificity of 86.17%. The genes identified with the decision trees may be involved in processes like those of Alzheimer’s disease, such as in the inflammation process, amyloid pathologies, and related to type 2 diabetes mellitus.

BioMedInformatics doi: 10.3390/biomedinformatics5020025

Authors: Maria Teresa Colangelo Stefano Guizzardi Marco Meleti Elena Calciolari Carlo Galli

Background: Systematic reviewers face a growing body of biomedical literature, making early-stage article screening increasingly time-consuming. In this study, we assessed six large language models (LLMs)—OpenHermes, Flan T5, GPT-2, Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o—for their ability to identify randomized controlled trials (RCTs) in datasets of increasing difficulty. Methods: We first retrieved articles from PubMed and used all-mpnet-base-v2 to measure semantic similarity to known target RCTs, stratifying the collection into quartiles of descending relevance. Each LLM then received either verbose or concise prompts to classify articles as “Accepted” or “Rejected”. Results: Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o consistently achieved high recall, though their precision varied in the quartile with the highest similarity, where false positives increased. By contrast, smaller or older models struggled to balance sensitivity and specificity, with some over-including irrelevant studies or missing key articles. Importantly, multi-stage prompts did not guarantee performance gains for weaker models, whereas single-prompt approaches proved effective for advanced LLMs. Conclusions: These findings underscore that both model capability and prompt design strongly affect classification outcomes, suggesting that newer LLMs, if properly guided, can substantially expedite systematic reviews.

BioMedInformatics doi: 10.3390/biomedinformatics5020024

Authors: Seyedmohsen Dehghanojamahalleh Peshala Thibbotuwawa Gamage Mohammad Ahmed Cassondra Petersen Brianna Matthew Kesha Hyacinth Yasith Weerasinghe Ersoy Subasi Munevver Mine Subasi Mehmet Kaya

(1) Background: Blood pressure (BP) variability is an important risk factor for cardiovascular diseases. Still, existing BP monitoring methods often require periodic cuff-based measurements, raising concerns about their accuracy and convenience. This study aims to develop a subject-independent, cuff-less BP estimation method using finger and toe photoplethysmography (PPG) signals combined with an electrocardiogram (ECG) without the need for an initial cuff-based measurement. (2) Methods: A customized measurement system was used to record 80 readings from human subjects. Fifteen features with the highest dependency on the reference BP, including time and morphological characteristics of PPG and subject information, were analyzed. A multivariate regression model was employed to estimate BP. (3) Results: The results showed that incorporating toe PPG signals improved the accuracy of BP estimation, reducing the mean absolute error (MAE). Using both finger and toe PPG signals resulted in an MAE of 9.63 ± 12.54 mmHg for systolic BP and 6.76 ± 8.38 mmHg for diastolic BP, providing the lowest MAE compared to previous methods. (4) Conclusions: This study is the first to integrate toe PPG for more accurate BP estimation and proposes a method that does not require an initial cuff-based BP measurement, offering a promising approach for non-invasive, continuous BP monitoring.

BioMedInformatics doi: 10.3390/biomedinformatics5020023

Authors: Rumana Islam Mohammed Tarique

Background: This work presents an artificial intelligence-based algorithm for detecting Parkinson’s disease (PD) from voice signals. The detection of PD at pre-symptomatic stages is imperative to slow disease progression. Speech signal processing-based PD detection can play a crucial role here, as it has been reported in the literature that PD affects the voice quality of patients at an early stage. Hence, speech samples can be used as biomarkers of PD, provided that suitable voice features and artificial intelligence algorithms are employed. Methods: Advanced signal-processing techniques are used to extract audio features from the sustained vowel ‘/a/’ sound. The extracted audio features include baseline features, intensities, formant frequencies, bandwidths, vocal fold parameters, and Mel-frequency cepstral coefficients (MFCCs) to form a feature vector. Then, this feature vector is further enriched by including wavelet-based features to form the second feature vector. For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. Results: The results demonstrate that the inclusion of wavelet-based voice features enhances the performance of both the SVM and kNN models for PD detection. However, kNN provides better accuracy, detection speed, training time, and misclassification cost than SVM. Conclusions: This work concludes that wavelet-based voice features are important for detecting neurodegenerative diseases like PD. These wavelet features can enhance the classification performance of machine learning models. This work also concludes that kNN is recommendable over SVM for the investigated voice features, despite the inclusion and exclusion of the wavelet features.

BioMedInformatics doi: 10.3390/biomedinformatics5020022

Authors: Christos Kokkotis Ioannis Kansizoglou Theodoros Stampoulis Erasmia Giannakou Panagiotis Siaperas Stavros Kallidis Maria Koutra Christina Koutra Anastasia Beneka Evangelos Bebetsos

Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT.

BioMedInformatics doi: 10.3390/biomedinformatics5020021

Authors: Vasileia Paschaloudi Dimitris Fotopoulos Ioanna Chouvarda

Background: Radiomic features have been extensively used with machine learning and other Artificial Intelligence methods in medical imaging problems. Coronavirus Disease 2019 (COVID-19), which has been spreading worldwide since 2020, has motivated scientists to develop automatic COVID-19 recognition systems, to enhance the clinical routine in overcrowded hospitals. Purpose: To develop an automated system of recognizing COVID-19 and Community-Acquired Pneumonia (CAP) using radiomic features extracted from whole lung chest Computed Tomography (CT) images. Radiomic feature extraction from whole lung CTs simplifies the image segmentation for the malignancy region of interest (ROI). Methods: In this work, we used radiomic features extracted from CT images representing whole lungs to train various machine learning models that are capable of identifying COVID-19 images, CAP images and healthy cases. The CT images were derived from an open access data set, called COVID-CT-MD, containing 76 Normal cases, 169 COVID-19 cases and 60 CAP cases. Results: Four two-class models and one three-class model were developed: Normal–COVID, COVID–CAP, Normal–CAP, Normal–Disease and Normal–COVID–CAP. Different algorithms and data augmentation were used to train each model 20 times on a different data set split, and, finally, the model with the best average performance was selected for each case. The performance metrics of Accuracy, Sensitivity and Specificity were used to assess the performance of the different systems. Since COVID-19 and CAP share similar characteristics, it is challenging to develop a model that can distinguish these diseases. Result: The results were promising for the models finally selected for each case. The accuracy for the independent test set was 83.11% in the Normal–COVID case, 88.77% in the COVID–CAP case, 93.97% in the Normal–CAP case and 94.13% in the Normal–Disease case, when referring to two-class cases, while, in the three-class case, the accuracy was 78.55%. Conclusion: The results obtained suggest that radiomic features extracted from whole lung CT images can be successfully used to distinguish COVID-19 from other pneumonias and normal lung cases.

BioMedInformatics doi: 10.3390/biomedinformatics5020020

Authors: Anh T. Tran Tal Zeevi Seyedmehdi Payabvash

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its promise, challenges such as image heterogeneity across different centers, variability in acquisition protocols and scanners, and sensitivity to artifacts hinder the reliability and clinical integration of deep learning models. Addressing these issues is critical for ensuring accurate and practical AI-powered neuroimaging applications. We reviewed and summarized the strategies for improving the robustness and generalizability of deep learning models for the segmentation and classification of neuroimages. This review follows a structured protocol, comprehensively searching Google Scholar, PubMed, and Scopus for studies on neuroimaging, task-specific applications, and model attributes. Peer-reviewed, English-language studies on brain imaging were included. The extracted data were analyzed to evaluate the implementation and effectiveness of these techniques. The study identifies key strategies to enhance deep learning in neuroimaging, including regularization, data augmentation, transfer learning, and uncertainty estimation. These approaches address major challenges such as data variability and domain shifts, improving model robustness and ensuring consistent performance across diverse clinical settings. The technical strategies summarized in this review can enhance the robustness and generalizability of deep learning models for segmentation and classification to improve their reliability for real-world clinical practice.

BioMedInformatics doi: 10.3390/biomedinformatics5020019

Authors: Alexis Sagonas Avgi E. Apostolakou Zoi I. Litou Marianna H. Antonelou Vassiliki A. Iconomidou

Background: Exosomes are a group of extracellular vesicles that are released by almost all mammalian cell types and engage in intracellular communication. Studies conducted in recent years have shown that exosomes are involved in a variety of diseases, where they may act as “vehicles” for the transmission of biomolecules and biomolecular information. Amyloidoses constitute a critical subgroup of these diseases, caused by extracellular deposition or intracellular inclusions of insoluble protein fibrils in cells and tissues. However, how exosomes are involved in these diseases remains largely unexplored. Methods: To detect possible links between amyloid proteins and exosomes, protein data from amyloidosis-isolated exosomes were collected and visualized using biological networks. Results: This biomedical informatics approach for the analysis of interaction networks, in combination with the existing literature, highlighted the involvement of exosomes in amyloidosis while strengthening existing hypotheses regarding their mechanism of action. Conclusion: This work is focused on exosomes from patients with Alzheimer’s disease and identifies important amyloidogenic proteins found in exosomes. These proteins can be used for future research in the field of exosome-based biomarkers of amyloidosis and potential prognostic or preventive approaches.

BioMedInformatics doi: 10.3390/biomedinformatics5020018

Authors: Fnu Neha Deepshikha Bhati Deepak Kumar Shukla

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential.

BioMedInformatics doi: 10.3390/biomedinformatics5020017

Authors: Lisa Anita De Santi Francesca Orlandini Vincenzo Positano Laura Pistoia Francesco Sorrentino Giuseppe Messina Maria Grazia Roberti Massimiliano Missere Nicolò Schicchi Antonino Vallone Maria Filomena Santarelli Alberto Clemente Antonella Meloni

Survival analysis is a statistical approach widely employed to model the time of an event, such as a patient’s death. Classical approaches include the Kaplan–Meier estimator and Cox proportional hazards regression, which assume a linear relationship between the model’s covariates. However, the linearity assumption might pose challenges with high-dimensional data, thus stimulating interest in performing survival analysis using neural network models. In the present work, we implemented a deep Cox neural network (Cox-net) to predict the time of a cardiac event using patient data collected from the Myocardial Iron Overload in Thalassemia (MIOT) project. Cox-net achieved a concordance index (c-index) of 0.812 ± 0.036, outperforming the classical Cox regression (0.790 ± 0.040), and it demonstrated resilience to varying levels of censored patients. A permutation feature importance analysis identified fibrosis and sex as the most significant predictors, aligning with clinical knowledge. Cox-net was able to represent the nonlinear relationships between covariates and maintain reliable survival curve predictions in datasets with a large number of censored patients, making it a promising tool for determining the appropriate clinical pathway for thalassemic patients.

BioMedInformatics doi: 10.3390/biomedinformatics5010016

Authors: Giorgia Francesca Saraceno Erika Cione

Background: Critical studies have unwaveringly established the importance of peculiar single-nucleotide polymorphisms (SNPs) in apolipoproteins (Apos) genes as genetic risk factors for dyslipidemias and their related comorbidities. In this study, we employed in silico approaches to analyze mutations in Apos. Methods: A comprehensive set of computational tools was utilized. The tools for predictions derived from sequence analysis were: SIFT, PolyPhen-2, FATHMM and SNPs&GO; The tools for structure analysis were: mCSM, DynaMut2, MAESTROweb, and PremPS; for prediction of pathogenic potential were: MutPred2, and PhD-SNP; for profiling of aggregation propensity were: Camsol, and Aggrescan3D 2.0, and lastly, for residual frustration analysis, the Frustratometer was used. These approaches assess variant effects on protein structure, stability, and function. Results: We identified seventeen SNPs in total, twelve for ApoB, one for ApoC2, one for ApoC3, and three for ApoE, representing 70%, 6%, 6% and 18%, respectively. The pathogenity of ApoE, was highlighted in two SNPs the rs769452 with amino acid replacement L46P, and rs769455 with amino acid replacement R163C. The aggregation/solubility analysis revealed that the L46P leads to a decrease in ApoE aggregation. The R163C, showed a decrease in solubility in one of two tools used, resulting in destabilizing effects altering its solubility. Conclusions: The two mutations in ApoE studied with the in silico methodologies identified clinically significant genetic variants, highlighting the robustness of the integrated approach. The future direction of the research is to create a multiplex panel with the SNPs identified here in APOE and expanding to other proteins to have a panel genetic risk assessment and disease prediction in which ApoE correlates.

BioMedInformatics doi: 10.3390/biomedinformatics5010015

Authors: Maria Teresa Colangelo Stefano Guizzardi Marco Meleti Elena Calciolari Carlo Galli

Large language models (LLMs) have emerged as powerful tools for (semi-)automating the initial screening of abstracts in systematic reviews, offering the potential to significantly reduce the manual burden on research teams. This paper provides a broad overview of prompt engineering principles and highlights how traditional PICO (Population, Intervention, Comparison, Outcome) criteria can be converted into actionable instructions for LLMs. We analyze the trade-offs between “soft” prompts, which maximize recall by accepting articles unless they explicitly fail an inclusion requirement, and “strict” prompts, which demand explicit evidence for every criterion. Using a periodontics case study, we illustrate how prompt design affects recall, precision, and overall screening efficiency and discuss metrics (accuracy, precision, recall, F1 score) to evaluate performance. We also examine common pitfalls, such as overly lengthy prompts or ambiguous instructions, and underscore the continuing need for expert oversight to mitigate hallucinations and biases inherent in LLM outputs. Finally, we explore emerging trends, including multi-stage screening pipelines and fine-tuning, while noting ethical considerations related to data privacy and transparency. By applying systematic prompt engineering and rigorous evaluation, researchers can optimize LLM-based screening processes, allowing for faster and more comprehensive evidence synthesis across biomedical disciplines.

BioMedInformatics doi: 10.3390/biomedinformatics5010014

Authors: Evangelia Tsakanika Vasileios Tsoukas Athanasios Kakarountas Vasileios Kokkinos

Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population. The criticality of seizure occurrence and associated risks, combined with the overwhelming need for more precise and innovative treatment methods, has led to the development of invasive neurostimulation devices programmed to detect and apply electrical stimulation therapy to suppress seizures and reduce the seizure burden. Tiny Machine Learning (TinyML) is a rapidly growing branch of machine learning. One of its key characteristics is the ability to run machine learning algorithms without the need for high computational complexity and powerful hardware resources. The featured work utilizes TinyML technology to implement an algorithm that can be integrated into the microprocessor of an implantable closed-loop brain neurostimulation system to accurately detect seizures in real-time by analyzing intracranial EEG (iEEG) signals. Methods: A dataset containing iEEG signal values from both non-epileptic and epileptic individuals was utilized for the implementation of the proposed algorithm. Appropriate data preprocessing was performed, and two training datasets with 1000 records of non-epileptic and epileptic iEEG signals were created. A test dataset with an independent dataset of 500 records was also created. The web-based platform Edge Impulse was used for model generation and visualization, and different model architectures were explored and tested. Finally, metrics of accuracy, confusion matrices, and ROC curves were used to evaluate the performance of the model. Results: Our model demonstrated high performance, achieving 98% and 99% accuracy on the validation and test EEG datasets, respectively. Our results support the use of TinyML technology in closed-loop neurostimulation devices for epilepsy, as it contributes significantly to the speed and accuracy of seizure detection. Conclusions: The proposed TinyML model demonstrated reliable seizure detection in real-time by analyzing EEG signals and distinguishing epileptic activity from normal brain electrical activity. These findings highlight the potential of TinyML in closed-loop neurostimulation systems for epilepsy, enhancing both speed and accuracy in seizure detection.

BioMedInformatics doi: 10.3390/biomedinformatics5010013

Authors: Michael H. Terrefortes-Rosado Andrea V. Nieves-Rivera Humberto Ortiz-Zuazaga Marie Lluberes-Contreras

Background: Variations in the states of Gene Regulatory Networks significantly influence disease outcomes and drug development. Boolean Networks serve as a tool to conceptualize and understand the complex relationships between genes. Threshold computation methods are used for the binarization of gene expression and the Boolean representation of its Gene Regulatory Network. This study aims to provide a platform that facilitates the exploration of the impact of different threshold computation methods on the binarization of gene expression and the subsequent Boolean representation of Gene Regulatory Networks. Methods: Threshold computation methods are implemented for binarizing gene expression, enabling the Boolean representation of the Gene Regulatory Networks. Variations in gene expression discretization and threshold computation methods often lead to differing Boolean representations, which may affect the subsequent analysis. Lluberes proposed a framework for analyzing gene expression when binarization varies based on these factors. This theoretical framework was implemented using the Python Dash framework. Results: A visualization tool has been developed to implement this framework. The tool allows users to upload gene expression datasets and interact with a dashboard to explore gene expression binarization and the inferred Boolean Networks. Conclusions: The developed visualization tool provides a platform that facilitates the exploration of how different binarization methods impact the interpretation of Gene Regulatory Networks, offering insights for disease research and drug development.

BioMedInformatics doi: 10.3390/biomedinformatics5010012

Authors: Moaiz Abrar Yusuf Sermet Ibrahim Demir

Background: Large Language Models (LLMs) have demonstrated strong performances in clinical question-answering (QA) benchmarks, yet their effectiveness in addressing real-world consumer medical queries remains underexplored. This study evaluates the capabilities and limitations of LLMs in answering consumer health questions using the MedRedQA dataset, which consists of medical questions and answers by verified experts from the AskDocs subreddit. Methods: Five LLMs-GPT-4o mini, Llama 3.1-70B, Mistral-123B, Mistral-7B, and Gemini-Flash were assessed using a cross-evaluation framework. Each model generated responses to consumer queries and their outputs were evaluated by every model by comparing them with expert responses. Human evaluation was used to assess the reliability of models as evaluators. Results: GPT-4o mini achieved the highest alignment with expert responses according to four out of the five models’ judges, while Mistral-7B scored the lowest according to three out of five models’ judges. Overall, model responses show low alignment with expert responses. Conclusions: Current small or medium sized LLMs struggle to provide accurate answers to consumer health questions and must be significantly improved.

BioMedInformatics doi: 10.3390/biomedinformatics5010011

Authors: Susanne F. Awad Diego F. Cuadros

Background: Understanding the dynamics of HIV transmission in heterogeneous populations is crucial for effective prevention strategies. This study introduces the Risk Modulation Point (RMP), a novel threshold identifying where HIV transmission transitions from unsustainable spread to self-sustaining epidemic dynamics. Methods: Using a deterministic, risk-stratified compartmental model, we examined HIV transmission across populations stratified into 100–200 risk groups, each characterized by behavioral heterogeneity modeled through a power-law distribution. The model captures key features of HIV progression, with simulations conducted across high- (~20%), moderate- (~5%), and low (~0.2%)-prevalence regimes. Results: Our findings reveal universal patterns in HIV dynamics. The RMP marks a consistent threshold across scenarios, separating low-risk groups where transmission is minimal from higher-risk groups sustaining the epidemic. Logistic growth in HIV prevalence across risk groups, with sharp transitions near the RMP, was observed universally. The force of infection follows power-law scaling, directly reflecting the level and nature of risk behavior within each group. Importantly, the location of the RMP remains largely invariant to the underlying sexual risk distribution, population resolution, and mixing patterns, making it applicable across both generalized and concentrated epidemics. Conclusion: The RMP framework offers actionable public health insights. It identifies key populations and transition regions for targeted interventions such as antiretroviral therapy and pre-exposure prophylaxis. By tracking shifts in the RMP, it also serves as an early warning indicator for epidemic transitions, guiding resource allocation and monitoring. The focus of the model on intrinsic epidemic dynamics, excluding external interventions, highlights its utility in uncovering fundamental transmission patterns. This study bridges theoretical modeling and practical application, providing a flexible framework for understanding HIV and other stratified epidemics. The findings advance HIV modeling by revealing generic patterns that transcend specific contexts, supporting data-driven public health strategies.

BioMedInformatics doi: 10.3390/biomedinformatics5010010

Authors: Ioannis Prokopiou Panagiota Spyridonos

Background: In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet limited data and high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing computational costs, although applying these models can still be resource intensive. This study aims to present flexible and computationally efficient architecture that leverages transfer learning and delivers highly accurate results across various medical imaging problems. Methods: We evaluated three datasets with varying similarities to ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), and the Shenzhen and Montgomery CXR Set (lung segmentation). An ablation study on ISIC 2018 tested various pre-trained backbones, architectures, and loss functions. Results: The optimal configuration—DeepLabV3+ with a pre-trained ResNet50 backbone and Log-Cosh Dice loss—was validated on the remaining datasets, achieving state-of-the-art results. Conclusion: Computationally simpler architectures can deliver robust performance without extensive resources, establishing DeepLabV3+ with the ResNet50 as a baseline for future studies. In the medical domain, enhancing data quality is more critical for improving segmentation accuracy than increasing model complexity.

BioMedInformatics doi: 10.3390/biomedinformatics5010009

Authors: Houda Bendani Nasma Boumajdi Lahcen Belyamani Azeddine Ibrahimi

Background: Tumor mutation burden (TMB), a genomic biomarker, has proven to be a strong predictor of immunotherapy response but is not widely adopted. This study investigates the association between TMB and immune checkpoint inhibitors (ICIs) response in TNBC patients. Methods: From the TCGA database, patients were stratified into two levels based on TMB and validated using survival analysis. Then, four machine learning models were trained to classify TNBC patients based on histological features into high and low TMB. To further validate our approach, we compared the genomic landscapes of both groups, identified differentially expressed genes (DEGs), and performed pathway enrichment analysis. Results: Our findings revealed a significant association between TMB and ICI response in TNBC. Random forest model effectively classified TNBC patients based on the representative histological features and clinical data with an accuracy of 0.82 on the validation set. The genomic analysis revealed that FAT3, TTN, and DYNC2H1 had a significantly high mutation rate in the TMB groups. Genes impacting cancer progression and immunogenicity were identified in the DEG analysis as IGF2, CLEC3A, and CASC9. Conclusions: This study constructs a model to identify suitable TNBC patients for immunotherapy and highlights the potential role of TMB associated with genomic alterations in predicting immune response in TNBC.

BioMedInformatics doi: 10.3390/biomedinformatics5010008

Authors: Federico Pacchioni Giacomo Germagnoli Marta Calbi Giulia Agostoni Jacopo Sapienza Federica Repaci Michele D’Incalci Marco Spangaro Roberto Cavallaro Marta Bosia

EEG microstates are brief, stable topographical configurations of brain activity that provide insights into alterations in brain function and connectivity. Anomalies in microstates are associated with different neuropsychiatric conditions, especially schizophrenia. Recent advances in both EEG techniques and machine learning point to the potential role of microstates as diagnostic markers for psychotic disorders. This systematic review aims to gather current knowledge on machine learning applied to EEG microstate analysis in psychotic disorders. Following PRISMA guidelines, we searched Scopus, PubMed, and Scholar databases, including 10 studies. Overall results show that EEG microstates can be used to accurately classify diagnoses within the psychosis spectrum, across all stages, outperforming models based on conventional EEG measures, with a prominent role of microstate D. One study also suggests that microstate anomalies may be directly linked to symptom severity. Integrating EEG microstates with machine learning shows promise in improving our understanding of psychotic disorders and developing more precise diagnostic tools.

BioMedInformatics doi: 10.3390/biomedinformatics5010007

Authors: Georgios Lekkas Eleni Vrochidou George A. Papakostas

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data.

BioMedInformatics doi: 10.3390/biomedinformatics5010006

Authors: Yufeng Zheng

Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. The CNN processes video frames one by one while convLSTM and ViViT handle sequences of frames. These high-resolution temporal features are fused to predict HR, BP, and SpO2, capturing their dynamic variations effectively. Results: The dataset encompasses 891 subjects of diverse races and ages, and preprocessing includes facial detection and data normalization. Experimental results demonstrate high accuracies in predicting HR, SpO2, and BP using the proposed hybrid models. Discussion: Facial images can be easily captured using smartphones, which offers an economical and convenient solution for vital sign monitoring, particularly beneficial for elderly individuals or during outbreaks of contagious diseases like COVID-19. The proposed models were only validated on one dataset. However, the dataset (size, representation, diversity, balance, and processing) plays an important role in any data-driven models including ours. Conclusions: Through experiments, we observed the hybrid model’s efficacy in predicting vital signs such as HR, SpO2, SBP, and DBP, along with demographic variables like sex and age. There is potential for extending the hybrid model to estimate additional vital signs such as body temperature and respiration rate.

BioMedInformatics doi: 10.3390/biomedinformatics5010005

Authors: Sara Reis Luís Pinto-Coelho Maria Sousa Mariana Neto Marta Silva

Background: Human emotions are subjective psychophysiological processes that play an important role in the daily interactions of human life. Emotions often do not manifest themselves in isolation; people can experience a mixture of them and may not express them in a visible or perceptible way; Methods: This study seeks to uncover EEG patterns linked to emotions, as well as to examine brain activity across emotional states and optimise machine learning techniques for accurate emotion classification. For these purposes, the DEAP dataset was used to comprehensively analyse electroencephalogram (EEG) data and understand how emotional patterns can be observed. Machine learning algorithms, such as SVM, MLP, and RF, were implemented to predict valence and arousal classifications for different combinations of frequency bands and brain regions; Results: The analysis reaffirms the value of EEG as a tool for objective emotion detection, demonstrating its potential in both clinical and technological contexts. By highlighting the benefits of using fewer electrodes, this study emphasises the feasibility of creating more accessible and user-friendly emotion recognition systems; Conclusions: Further improvements in feature extraction and model generalisation are necessary for clinical applications. This study highlights not only the potential of emotion classification to develop biomedical applications, but also to enhance human–machine interaction systems.

BioMedInformatics doi: 10.3390/biomedinformatics5010004

Authors: Taylor Simoes-Gomes Stéphane Potvin Sabrina Giguère Mélissa Beaudoin Kingsada Phraxayavong Alexandre Dumais

Background: The advent of virtual reality in psychiatry presents a wealth of opportunities for a variety of psychopathologies. Avatar Interventions are dialogic and experiential treatments integrating personalized medicine with virtual reality (VR), which have shown promising results by enhancing the emotional regulation of their participants. Notably, Avatar Therapy for the treatment of auditory hallucinations (i.e., voices) allows patients to engage in dialogue with an avatar representing their most persecutory voice. In addition, Avatar Intervention for cannabis use disorder involves an avatar representing a significant person in the patient’s consumption. In both cases, the main goal is to modify the problematic relationship and allow patients to regain control over their symptoms. While results are promising, its potential to be applied to other psychopathologies, such as major depression, is an exciting area for further exploration. In an era where VR interventions are gaining popularity, the present study aims to investigate whether technological advancements could overcome current limitations, such as avatar realism, and foster a deeper immersion into virtual environments, thereby enhancing participants’ sense of presence within the virtual world. A newly developed virtual reality platform was compared to the current platform used by our research team in past and ongoing studies. Methods: This study involved 43 subjects: 20 healthy subjects and 23 subjects diagnosed with severe mental disorders. Each participant interacted with an avatar using both platforms. After each immersive session, questionnaires were administered by a graduate student in a double-blind manner to evaluate technological advancements and user experiences. Results: The findings indicate that the new technological improvements allow the new platform to significantly surpass the current platform as per multiple subjective parameters. Notably, the new platform was associated with superior realism of the avatar (d = 0.574; p < 0.001) and the voice (d = 1.035; p < 0.001), as well as enhanced lip synchronization (d = 0.693; p < 0.001). Participants reported a significantly heightened sense of presence (d = 0.520; p = 0.002) and an overall better immersive experience (d = 0.756; p < 0.001) with the new VR platform. These observations were true in both healthy subjects and participants with severe mental disorders. Conclusions: The technological improvements generated a heightened sense of presence among participants, thus improving their immersive experience. These two parameters could be associated with the effectiveness of VR interventions and future studies should be undertaken to evaluate their impact on outcomes.

BioMedInformatics doi: 10.3390/biomedinformatics5010003

Authors: Daniel Nasef Demarcus Nasef Viola Sawiris Peter Girgis Milan Toma

(1) Background: The exploration of various machine learning (ML) algorithms for classifying the state of Lumbar Intervertebral Discs (IVD) in orthopedic patients is the focus of this study. The classification is based on six key biomechanical features of the pelvis and lumbar spine. Although previous research has demonstrated the effectiveness of ML models in diagnosing IVD pathology using imaging modalities, there is a scarcity of studies using biomechanical features. (2) Methods: The study utilizes a dataset that encompasses two classification tasks. The first task classifies patients into Normal and Abnormal based on their IVDs (2C). The second task further classifies patients into three groups: Normal, Disc Hernia, and Spondylolisthesis (3C). The performance of various ML models, including decision trees, support vector machines, and neural networks, is evaluated using metrics such as accuracy, AUC, recall, precision, F1, Kappa, and MCC. These models are trained on two open-source datasets, using the PyCaret library in Python. (3) Results: The findings suggest that an ensemble of Random Forest and Logistic Regression models performs best for the 2C classification, while the Extra Trees classifier performs best for the 3C classification. The models demonstrate an accuracy of up to 90.83% and a precision of up to 91.86%, highlighting the effectiveness of ML models in diagnosing IVD pathology. The analysis of the weight of different biomechanical features in the decision-making processes of the models provides insights into the biomechanical changes involved in the pathogenesis of Lumbar IVD abnormalities. (4) Conclusions: This research contributes to the ongoing efforts to leverage data-driven ML models in improving patient outcomes in orthopedic care. The effectiveness of the models for both diagnosis and furthering understanding of Lumbar IVD herniations and spondylolisthesis is outlined. The limitations of AI use in clinical settings are discussed, and areas for future improvement to create more accurate and informative models are suggested.

BioMedInformatics doi: 10.3390/biomedinformatics5010002

Authors: Stavros Antonopoulos Manolis Wallace Vassilis Poulopoulos

Background: This study aims to revolutionize traditional pediatric vaccination protocols by integrating virtual reality (VR) technology. The purpose is to minimize discomfort in children, ages 2–12, during vaccinations by immersing them in a specially designed VR short story that aligns with the various stages of the clinical vaccination process. In our approach, the child dons a headset during the vaccination procedure and engages with a virtual reality (VR) short story that is specifically designed to correspond with the stages of a typical vaccination process in a clinical setting. Methods: A two-phase clinical trial was conducted to evaluate the effectiveness of the VR intervention. The first phase included 242 children vaccinated without VR, serving as a control group, while the second phase involved 97 children who experienced VR during vaccination. Discomfort levels were measured using the VACS (VAccination disComfort Scale) tool. Statistical analyses were performed to compare discomfort levels based on age, phases of vaccination, and overall experience. Results: The findings revealed significant reductions in discomfort among children who experienced VR compared to those in the control group. The VR intervention demonstrated superiority across multiple dimensions, including age stratification and different stages of the vaccination process. Conclusions: The proposed VR framework significantly reduces vaccination-related discomfort in children. Its cost-effectiveness, utilizing standard or low-cost headsets like Cardboard devices, makes it a feasible and innovative solution for pediatric practices. This approach introduces a novel, child-centric enhancement to vaccination protocols, improving the overall experience for young patients.

BioMedInformatics doi: 10.3390/biomedinformatics5010001

Authors: Simon Lebech Cichosz Clara Bender Ole Hejlesen

Objective: This study aimed to investigate the utilization of patterns derived from physical activity monitoring (PAM) for the identification of individuals at risk of type 2 diabetes mellitus (T2DM) through an at-home screening approach employing machine learning techniques. Methods: Data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) were scrutinized, focusing on the PAM component. The primary objective involved the identification of diabetes, characterized by an HbA1c ≥ 6.5% (48 mmol/mol), while the secondary objective included individuals with prediabetes, defined by an HbA1c ≥ 5.7% (39 mmol/mol). Features derived from PAM, along with age, were utilized as inputs for an XGBoost classification model. SHapley Additive exPlanations (SHAP) was employed to enhance the interpretability of the models. Results: The study included 7532 subjects with both PAM and HbA1c data. The model, which solely included PAM features, had a test dataset ROC-AUC of 0.74 (95% CI = 0.72–0.76). When integrating the PAM features with age, the model’s ROC-AUC increased to 0.79 (95% CI = 0.78–0.80) in the test dataset. When addressing the secondary target of prediabetes, the XGBoost model exhibited a test dataset ROC-AUC of 0.80 [95% CI; 0.79–0.81]. Conclusions: The objective quantification of physical activity through PAM yields valuable information that can be employed in the identification of individuals with undiagnosed diabetes and prediabetes.

BioMedInformatics doi: 10.3390/biomedinformatics4040129

Authors: Sotiris Raptis Christos Ilioudis Kiki Theodorou

Background: Lung cancer still maintains the leading position among causes of death in the world; the process of early detection surely contributes to changes in the survival of patients. Standard diagnostic methods are grossly insensitive, especially in the early stages. In this paper, radiomic features are discussed that can assure improved diagnostic accuracy through automated lung cancer detection by considering the important feature categories, such as texture, shape, and intensity, originating from the CT DICOM images. Methods: We developed and compared the performance of two machine learning models—DenseNet-201 CNN and XGBoost—trained on radiomic features with the ability to identify malignant tumors from benign ones. Feature importance was analyzed using SHAP and techniques of permutation importance that enhance both the global and case-specific interpretability of the models. Results: A few features that reflect tumor heterogeneity and morphology include GLCM Entropy, shape compactness, and surface-area-to-volume ratio. These performed excellently in diagnosis, with DenseNet-201 producing an accuracy of 92.4% and XGBoost at 89.7%. The analysis of feature interpretability ascertains its potential in early detection and boosting diagnostic confidence. Conclusions: The current work identifies the most important radiomic features and quantifies their diagnostic significance through a properly conducted feature selection process reflecting stability analysis. This provides the blueprint for feature-driven model interpretability in clinical applications. Radiomics features have great value in the automated diagnosis of lung cancer, especially when combined with machine learning models. This might improve early detection and open personalized diagnostic strategies for precision oncology.