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Recent Advances in Biomedical Data Analysis
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Recent Advances in Biomedical Data Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 8434

Special Issue Editor

Prof. Dr. Josué Álvarez Borrego

E-Mail Website
Guest Editor
Department of Optics, Ensenada Center for Scientific Research and Higher Education, Ensenada C.P. 22860, Mexico
Interests: image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical data analysis is one of the most important branches of science, combining several fields of knowledge, such as biology, chemistry, medicine, and mathematics, among others. Conducting research in this area can help us to diagnose and treat diseases much more efficiently than by using traditional methods. This Special Issue explores the publication of new methodologies, which can be based on AI, that will help us implement effective solutions for the benefit of humanity.

For example, treating diseases by identifying malignant tumors in the breast, skin or any other part of the human body, as well studying resistance to certain types of antibodies, remains a challenge. Similarly, the study of proteins to develop vaccines or treat certain potentially congenital diseases is a significant issue that remains to be solved.

Scientific research within these diverse topics can lead to the development of new methodologies for data analysis in the field of biomedicine.

Data analysis in this field can lead to the development of new AI algorithms that can more efficiently solve biomedical problems.

Prof. Dr. Josué Álvarez Borrego
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • algorithms
  • image analysis
  • pattern recognition
  • data analysis
  • biomedicine

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Published Papers (7 papers)

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Research

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12 pages, 791 KB  
Article
Exploratory Evaluation of Diagnostic Accuracy and Temporal Reproducibility of Multimodal Large Language Models in the Image-Based Assessment of Oral Mucosal Lesions
by Lovro Dumančić, Marko Antonio Cug, Danica Vidović Juras, Luís Monteiro, Rui Albuquerque and Vlaho Brailo
Appl. Sci. 2026, 16(8), 4046; https://doi.org/10.3390/app16084046 - 21 Apr 2026
Viewed by 304
Abstract
Objective: The aim was to evaluate the diagnostic accuracy and temporal reproducibility of multimodal large language models (LLMs) in the image-based diagnosis of oral mucosal lesions. Materials and Methods: The study included 100 anonymized clinical photographs of oral mucosal conditions obtained from the [...] Read more.
Objective: The aim was to evaluate the diagnostic accuracy and temporal reproducibility of multimodal large language models (LLMs) in the image-based diagnosis of oral mucosal lesions. Materials and Methods: The study included 100 anonymized clinical photographs of oral mucosal conditions obtained from the archive of the Department of Oral Medicine, School of Dental Medicine, University of Zagreb. Images were categorized into four subgroups: physiological variations, benign mucosal lesions, oral potentially malignant disorders, and oral cancer (25 images each). Three multimodal LLMs (ChatGPT-5.1 Plus, Gemini 3 Pro, and Perplexity Pro) analyzed each image using an identical prompt and were required to provide a single most probable diagnosis based solely on visual features. To evaluate temporal reproducibility, the entire evaluation was repeated in three independent testing cycles conducted at one-month intervals. Diagnostic accuracy was compared using chi-square tests, while intra-model agreement across cycles was assessed using Fleiss’ kappa. Results: Gemini demonstrated the highest diagnostic accuracy, reaching 78% correct responses in cycles 2 and 3, significantly outperforming ChatGPT (55–57%) and Perplexity (28–31%) (p < 0.00001). Subgroup analyses showed similar trends, with Gemini achieving the highest accuracy across most lesion categories. Intra-model agreement across cycles was moderate for ChatGPT (κ = 0.525), fair for Gemini (κ = 0.338) and Perplexity (κ = 0.409). Gemini also showed the highest proportion of responses that remained correct across all three cycles (51%). Conclusions: Multimodal LLMs demonstrate promising diagnostic capabilities in the image-based assessment of oral mucosal lesions; however, variability in reproducibility highlights the need for cautious clinical implementation and further validation. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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24 pages, 6019 KB  
Article
EEG Microstate Comparative Model for Improving the Assessment of Prolonged Disorders of Consciousness: A Pilot Study
by Francesca Mancino, Monica Franzese, Marco Salvatore, Alfonso Magliacano, Salvatore Fiorenza, Anna Estraneo and Carlo Cavaliere
Appl. Sci. 2026, 16(2), 892; https://doi.org/10.3390/app16020892 - 15 Jan 2026
Viewed by 777
Abstract
Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding [...] Read more.
Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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50 pages, 24561 KB  
Article
Deep-Radiomic Fusion for Early Detection of Pancreatic Ductal Adenocarcinoma
by Georgios Lekkas, Eleni Vrochidou and George A. Papakostas
Appl. Sci. 2025, 15(24), 13024; https://doi.org/10.3390/app152413024 - 10 Dec 2025
Viewed by 1165
Abstract
Leveraging the complementary strengths of handcrafted radiomics and data-driven deep learning, this work develops and rigorously benchmarks three modeling streams (Models A, B and C) for pancreatic ductal adenocarcinoma (PDAC) detection on multiphase abdominal Computed Tomography (CT) scans. Model A distills hundreds of [...] Read more.
Leveraging the complementary strengths of handcrafted radiomics and data-driven deep learning, this work develops and rigorously benchmarks three modeling streams (Models A, B and C) for pancreatic ductal adenocarcinoma (PDAC) detection on multiphase abdominal Computed Tomography (CT) scans. Model A distills hundreds of PyRadiomics descriptors to sixteen interpretable features that feed a gradient-boosted machine learning model, achieving discrimination (external AUC ≈ 0.99) with excellent calibration. Model B adopts a 3-D CBAM-ResNet-18 trained under weighted cross-entropy and mixed precision; although less accurate in isolation, it yields volumetric Grad-CAM maps that localize the tumor and provide explainability. Model C explores two fusion strategies that merge radiomics and deep embeddings: (i) a two-stage “frozen-stream” variant that locks both feature extractors and learns only a lightweight gating block plus classifier, and (ii) a full end-to-end version that allows the CNN’s adaptor layer to co-train with the fusion head. The frozen approach surpasses the single stream, whereas the end-to-end model reports external AUC of 0.987, balanced sensitivity/specificity above 0.93, and a Brier score below 0.05, while preserving clear Grad-CAM alignment with radiologist-drawn masks. Results demonstrate that a carefully engineered deep-radiomic fusion pipeline can deliver accurate, well-calibrated and interpretable PDAC triage directly from routine CT. Our contributions include a stability-verified 16-feature radiomic signature, a novel deep-radiomic fusion design that improves robustness and interpretability across vendors and a fully guideline-aligned, openly released pipeline for reproducible PDAC detection on routine CT. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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17 pages, 3346 KB  
Article
Enhancing Tree-Based Machine Learning for Personalized Drug Assignment
by Katyna Sada Del Real and Angel Rubio
Appl. Sci. 2025, 15(19), 10853; https://doi.org/10.3390/app151910853 - 9 Oct 2025
Viewed by 1045
Abstract
Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug [...] Read more.
Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing machine learning models often fall short in selecting the best drug from multiple candidates. We present SEATS (Systematic Efficacy Assignment with Treatment Seats), which adapts conventional models like Random Forest and XGBoost for multiclass drug assignment by allocating probabilistic “treatment seats” to drugs based on efficacy. This approach helps models learn clinically relevant distinctions. Additionally, we assess an interpretable Optimal Decision Tree (ODT) model designed specifically for drug assignment. Trained on the BeatAML2 cohort and validated on the GDSC AML cell line dataset, integrating SEATS with Random Forest and XGBoost improved prediction accuracy and consistency. The ODT model offered competitive performance with clear, interpretable decision paths and minimal feature requirements, facilitating clinical use. SEATS reorients standard models towards personalized drug selection. Combined with the ODT framework it provides effective, interpretable strategies for precision oncology, underscoring the potential of tailored machine learning solutions in supporting real-world treatment decisions. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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14 pages, 2128 KB  
Article
Correlation Measures in Metagenomic Data: The Blessing of Dimensionality
by Alessandro Fuschi, Alessandra Merlotti, Thi Dong Binh Tran, Hoan Nguyen, George M. Weinstock and Daniel Remondini
Appl. Sci. 2025, 15(15), 8602; https://doi.org/10.3390/app15158602 - 2 Aug 2025
Viewed by 1490
Abstract
Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the [...] Read more.
Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathways, and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly in light of the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson’s correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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15 pages, 2194 KB  
Article
Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis
by Luis Felipe López-Ávila and Josué Álvarez-Borrego
Appl. Sci. 2025, 15(11), 5860; https://doi.org/10.3390/app15115860 - 23 May 2025
Cited by 1 | Viewed by 1615
Abstract
Automated skin lesion classification using machine learning techniques is crucial for early and accurate skin cancer detection. This study proposes a hybrid method combining the Hermite, Radial Fourier–Mellin, and Hilbert transform to extract comprehensive features from skin lesion images. By separating the images [...] Read more.
Automated skin lesion classification using machine learning techniques is crucial for early and accurate skin cancer detection. This study proposes a hybrid method combining the Hermite, Radial Fourier–Mellin, and Hilbert transform to extract comprehensive features from skin lesion images. By separating the images into red, green, and blue (RGB) channels and grayscale, unique textural and structural information specific to each channel is analyzed. The Hermite transform captures localized spatial features, while the Radial Fourier–Mellin and Hilbert transforms ensure global invariance to scale, translation, and rotation. Texture information for each channel is also obtained based on the Local Binary Pattern (LBP) technique. The proposed hybrid transform-based feature extraction was applied to multiple lesion classes using the International Skin Imaging Collaboration (ISIC) 2019 dataset, preprocessed with data augmentation. Experimental results demonstrate that the proposed method improves classification accuracy and robustness, highlighting its potential as a non-invasive AI-based tool for dermatological diagnosis. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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Review

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22 pages, 334 KB  
Review
Digital Twins in Neonatology: Current Applications and Future Directions: A Narrative Review
by Dimitra Savvidou, Niki Dermitzaki, Maria Baltogianni, Aikaterini Nikolaou and Vasileios Giapros
Appl. Sci. 2026, 16(5), 2198; https://doi.org/10.3390/app16052198 - 25 Feb 2026
Viewed by 674
Abstract
Digital Twins (DTs) are virtual, patient-specific representations that integrate real-time data to model, predict, and optimize biological and clinical processes. In neonatology, DTs are gaining attention as powerful tools for managing the profound physiological complexity and variability of newborns, particularly preterm infants requiring [...] Read more.
Digital Twins (DTs) are virtual, patient-specific representations that integrate real-time data to model, predict, and optimize biological and clinical processes. In neonatology, DTs are gaining attention as powerful tools for managing the profound physiological complexity and variability of newborns, particularly preterm infants requiring intensive care. Emerging applications include cardiopulmonary modeling, prediction of sepsis and necrotizing enterocolitis (NEC), optimization of mechanical ventilation, individualized nutrition, and longitudinal monitoring of neuromotor development. This review synthesizes current research on neonatal digital twins, highlighting clinical use cases and ethical considerations. We discuss persistent challenges, including limited data availability, rapid developmental change, model validation, and regulatory oversight. Finally, we outline a roadmap for integrating DTs into neonatal intensive care units (NICUs) and identify future research priorities, including multi-organ integration, predictive closed-loop systems, and personalized life-course care trajectories. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Data Analysis)
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