BioMedInformatics

Background: Targeting Apolipoprotein E4 (ApoE4) represents a frontier in Alzheimer’s disease therapeutics. This study investigates the therapeutic potential of a nutraceutical panel (Polydatin, trans-resveratrol, luteolin, and PEA) by exploring their interaction with the ApoE4 EZ-482 cavity. Methods: Using a dual-platform docking strategy (SwissDock and Schrödinger Maestro) across three structural constructs. Results and Discussion: We identified the full-length protein (1–299) as the optimal target, showing a robust correlation between normalized docking scores (Spearman ρ = 0.79). Crucially, biophysical analysis via dynamic light scattering (DLS) revealed that the ApoE4–oxLDL complex exhibits a ζ-potential of −10.97 mV, a state prone to pathological aggregation. Luteolin and PEA effectively altered this electrostatic environment, inducing significant positive shifts to +2.15 mV and +1.05 mV, respectively. The alignment between computational rankings and experimental ζ-potential perturbations supports the predictive reliability of our model. These findings suggest that nutraceuticals can modulate the ApoE4–oxLDL biophysical profile and highlight that a full structural context is mandatory for developing effective ApoE4-targeted interventions. Full article

Background: This study examines the clinical efficacy of daratumumab when combined with other therapeutic agents in patients with multiple myeloma, with a focus on key outcomes including overall response, progression-free survival (PFS), and stringent complete response (sCR). Methods: A systematic literature search was conducted using PubMed, Web of Science, Scopus, and the Cochrane Library. Statistical analyses were performed with R software (version 4.2.2). Between-study heterogeneity was assessed using the Cochrane Q test and the I² statistic, while potential publication bias was evaluated through Egger’s regression test and visual inspection of funnel plots. Results: The meta-analysis revealed that daratumumab-containing regimens were associated with a 54.4% reduction in the risk of disease progression or death (hazard ratio[HR] 0.4558; 95% confidence-interval [CI]: 0.4031–0.5154). Similar results were observed using a random-effects model (HR 0.4667; 95% CI: 0.3771–0.5776), despite moderate heterogeneity (I² = 66.7%). Moreover, patients treated with daratumumab were approximately 2.4 times more likely to achieve a stringent complete response (odds-ratio[OR] 2.38; 95% CI: 1.80–3.15), with moderate heterogeneity across studies (I² = 58.2%). Conclusions: Incorporating daratumumab into standard therapy for multiple myeloma significantly enhances progression-free survival and the rate of stringent complete response. Despite some heterogeneity, the consistent positive outcomes support its use as an effective treatment option in clinical practice. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article