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AI-Driven Approaches to Diseases Detection and Diagnosis

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A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 10467

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Dr. Yannis Trakadis

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Department of Medical Genetics, McGill University Health Centre, Montreal, QC H3H 1P3, Canada
Interests: medical genetics; precision medicine; clinical integration of omics & AI tools
Special Issues, Collections and Topics in MDPI journals

Dr. Antonio Brunetti

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Guest Editor

Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
Interests: medical imaging; intelligent systems; bioengineering; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into healthcare is transforming the way that diseases are detected, diagnosed, and managed. Advances in machine learning, deep learning, and data-driven modeling are enabling earlier detection, higher accuracy, and more personalized decision support across a wide range of clinical contexts. From medical imaging and genomics to digital biomarkers and wearable sensors, AI offers unprecedented opportunities to accelerate diagnosis, reduce human error, and improve patient outcomes.

This Special Issue of Bioengineering aims to bring together cutting-edge research and review articles that explore the development, validation, and application of AI-driven approaches for disease detection and diagnosis. We welcome contributions spanning algorithm design, clinical implementation, multi-modal data integration, interpretability, ethical considerations, and real-world benchmarks. Submissions may include original research, methodological advances, comprehensive reviews, and case studies that highlight both opportunities and challenges in the field.

We particularly encourage interdisciplinary studies that bridge engineering, computer science, biomedical research, and clinical practice. By assembling diverse perspectives, this Special Issue seeks to advance the understanding and responsible adoption of AI technologies that have the potential to shape the future of medicine.

Kind regards,

Dr. Yannis Trakadis
Dr. Antonio Brunetti
Guest Editors

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

AI-driven diagnostics
disease detection
AI-driven treatment selection
precision medicine
+/− patient stratification
machine learning
deep learning
medical imaging
digital biomarkers
clinical decision support

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Related Special Issue

AI-Driven Approaches to Diseases Detection and Diagnosis—Second Edition in Bioengineering

Published Papers (10 papers)

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Research

14 pages, 1417 KB

Open AccessArticle

Resting-State Magnetoencephalography Functional Connectivity in Cervical Spondylotic Myelopathy: An MEG Study with SHAP-Based Interpretation

by Geng Zhao, Zhuang Miao, Shiqiang Zheng, Xinyu Liu and Xu Zhang

Bioengineering 2026, 13(9), 988; https://doi.org/10.3390/bioengineering13090988 - 27 Aug 2026

Viewed by 298

Abstract

The diagnosis of cervical spondylotic myelopathy (CSM) relies mainly on clinical symptoms and structural imaging, highlighting the need for objective functional biomarkers. This study investigated alterations in resting-state magnetoencephalography (MEG) functional connectivity in CSM and evaluated whether multiband weighted phase lag index (wPLI) features could distinguish CSM patients from healthy controls (HCs). Eyes-closed resting-state MEG data were acquired from 31 CSM patients and 32 HCs. Region-of-interest-level wPLI connectivity was calculated in the theta, alpha, beta, and gamma bands and used to train multiple machine learning classifiers. Model performance was assessed using nested group cross-validation, and SHapley Additive exPlanations (SHAP) were used to interpret the best-performing model. Patients with CSM exhibited frequency-specific connectivity alterations, particularly in the theta and gamma bands. Logistic regression achieved the best overall discriminative performance, and SHAP analysis indicated that classification was driven mainly by long-range theta-band connections and gamma-band connections involving the frontal pole. These findings suggest that CSM is associated with measurable reorganization of large-scale cortical networks and that resting-state MEG connectivity combined with explainable machine learning may provide a promising framework for exploring candidate neurophysiological biomarkers of CSM. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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22 pages, 2369 KB

Open AccessArticle

Machine Learning Integrating SERF-MEG and VEP for Diagnosis and Differential Diagnosis of Optic Neuropathies

by Helei Wang, Yuankun Qi, Yu Lou, Xu Zhang and Xinda Song

Bioengineering 2026, 13(8), 885; https://doi.org/10.3390/bioengineering13080885 - 31 Jul 2026

Viewed by 422

Abstract

Visual evoked potential (VEP) is widely used to assess optic nerve function, but its ability to differentiate optic neuropathies remains limited. This study investigated the diagnostic value of spin-exchange relaxation-free magnetoencephalography (SERF-MEG), alone and in combination with VEP, using machine learning. A total of 142 eyes, including 71 healthy controls (HC), 34 eyes with optic neuritis (ON), and 37 eyes with ischemic optic neuropathy (ION), were enrolled. Three feature sets were constructed from VEP, SERF-MEG, and their combination. Repeated stratified 5-fold nested cross-validation was used to evaluate nine supervised machine learning algorithms, and SHAP analysis was used to evaluate the interpretability of the models. The multilayer perceptron model obtained an AUC of 0.792 and an MCC of 0.470, whereas the combination VEP–MEG model performed the best in differentiating HC from optic neuropathies. The MEG model fared better than the VEP and combined models for distinguishing ON from ION, with logistic regression obtaining the greatest AUC of 0.847. Cortical network measurements and visually evoked response properties were found to contribute the most to model prediction, according to SHAP analysis. These results imply that SERF-MEG may enhance the diagnosis and differential diagnosis of optic neuropathies by offering supplementary neurophysiological data beyond traditional VEP. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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16 pages, 11705 KB

Open AccessArticle

SERF-MEG as a Functional Biomarker of Optic Nerve Injury: Agreement and Correlation with Visual Evoked Potentials

by Helei Wang, Yuankun Qi, Yu Lou, Xu Zhang and Xinda Song

Bioengineering 2026, 13(7), 830; https://doi.org/10.3390/bioengineering13070830 - 18 Jul 2026

Viewed by 696

Abstract

This study assessed the cross-modal agreement between spin-exchange relaxation-free magnetoencephalography (SERF-MEG) and conventional visual evoked potentials (VEPs) in patients with optic nerve injury using comparable pattern-reversal visual stimulation paradigms. Forty-five patients with optic neuritis (ON), ischemic optic neuropathy (ION), and traumatic optic neuropathy (TON) were enrolled, and a paired-eye design was applied to assess interocular electrophysiological changes between the affected and fellow eyes. Both modalities indicated consistent impairment of visual pathway function, reflected by reduced response amplitudes and prolonged latencies. For latency measures, MEG-derived M100 showed moderate directional concordance and a significant correlation with VEP P100, whereas amplitude measures demonstrated similar directional changes but limited quantitative correlations across modalities. Compared with the MaxP2P method, the channel-averaged method displayed higher stability and cross-modal concordance. These findings show that SERF-MEG can capture clinically relevant visual pathway impairment and may complement conventional VEP by providing additional information on cortical response patterns. Overall, SERF-MEG shows potential as an objective functional biomarker for neuro-ophthalmic assessment of optic nerve injury. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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22 pages, 33802 KB

Open AccessArticle

Active Learning Under Expert-Budget Constraints: A Human-in-the-Loop Pipeline for Diabetic Retinopathy Lesion Detection

by Hyeok Kim, Seok-Min Chang, Bo-Young Lim, Soo Young Lee and Ho-Gil Jung

Bioengineering 2026, 13(7), 762; https://doi.org/10.3390/bioengineering13070762 - 29 Jun 2026

Viewed by 534

Abstract

Early diagnosis of Diabetic Retinopathy (DR) is critical for preventing irreversible vision loss, but precise lesion annotation by ophthalmologists is the dominant cost in building any clinical-grade DR detection model. The structural problem in real hospital settings is not labeling cost per se, but expert availability: ophthalmologists’ time is bounded by clinical duties, so the active-learning (AL) cycle can iterate only a handful of times in practice. We frame this constraint explicitly and ask which AL designs work best under a tight expert budget. We propose Virtuous Cycle, a Human-in-the-Loop (HITL) pipeline that integrates (i) a YOLOv8x-based object detector for microaneurysms, hemorrhages, and exudates, (ii) four AL sampling strategies (Average Confidence, Random, Hybrid-Diversity, Monte Carlo Dropout), and (iii) an in-hospital annotation platform (Diavision Studio) in which clinicians refine AI pre-labels rather than draw from scratch. We evaluate Virtuous Cycle on a real-world fundus dataset from the National Medical Center (NMC) across eight AL rounds, expanding the labeled pool from 81 images (R0) to 481 images (R8) within the actual expert-time budget of two ophthalmologists. Across three independent random seeds, random sampling dominates at cold start (mean mAP@50

0.14 \to 0.25

over R0–R1), whereas Hybrid-Diversity converges to the highest mAP@50, Precision, and Recall by R7 (431 images; mAP@50

0.40

, Precision

0.55

, Recall

0.41

), with MC Dropout close behind; by R8, the labeled pool is exhausted and all strategies converge to the same final model. A clinician crossover analysis of 36 paired clinical images, controlling for per-clinician speed bias and per-image difficulty bias, shows no statistically significant difference in overall per-image labeling time between AI-assisted and manual annotation (

p = 0.52

), but a statistically significant increase in confirmed lesion detections under AI assistance (

p = 0.0058

), driven predominantly (84–

100 %

of the net increase) by microaneurysms, the lesion type most prone to being missed unaided. The results indicate that, under expert-budget constraints, AL strategy choice should be staged: random sampling for cold start, uncertainty-and-diversity sampling once the model has matured, and that AI assistance trades a modest, lesion-burden-dependent time cost for a measurable gain in the sensitivity of microaneurysm detection. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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18 pages, 12353 KB

Open AccessArticle

Decoding Visual Pathway Dysfunction with SERF-MEG: A Study in Patients with Optic Neuropathy

by Helei Wang, Yuankun Qi, Yu Lou, Xu Zhang and Xinda Song

Bioengineering 2026, 13(6), 694; https://doi.org/10.3390/bioengineering13060694 - 17 Jun 2026

Viewed by 540

Abstract

This study aimed to characterize cortical dysfunction and frequency-specific network reorganization following optic nerve injury using spin-exchange relaxation-free magnetoencephalography (SERF-MEG), and to assess the potential of MEG-derived multiscale features as sensitive functional biomarkers for clinical evaluation. In this prospective case–control study, SERF-MEG recordings were acquired during a pattern-reversal visual stimulation paradigm. Time-domain evoked components (M100/M135), global electrophysiological indices, energy-based metrics, and alpha- and beta-band phase-based functional connectivity were extracted. Network topology was quantified using graph-theoretical measures, including global and local efficiency, clustering coefficient, and assortativity. Group-level differences between patients and healthy controls were statistically analyzed. Patients showed significantly reduced M100/M135 amplitudes, prolonged M100 latency, and a lower early-component energy ratio. Functional connectivity was significantly decreased in the alpha and beta bands, accompanied by reduced global and local efficiency, mean strength, and clustering coefficient. Seed-based analyses revealed reduced connectivity predominantly in occipito-parietal and occipito-temporal pathways. SERF-MEG provides sensitive identification of cortical- and network-level functional impairments following optic nerve damage. MEG has significant clinical potential for disease diagnosis and therapy monitoring, providing a novel objective assessment tool for neuro-ophthalmological disorders. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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18 pages, 2784 KB

Open AccessArticle

Development and Internal Validation of an Explainable Machine Learning Model for Predicting Buttock Claudication After EVAR: A Dual-Center Cohort Study

by Yajing Li, Hongru Deng and Yongquan Gu

Bioengineering 2026, 13(6), 665; https://doi.org/10.3390/bioengineering13060665 - 8 Jun 2026

Viewed by 523

Abstract

Buttock claudication after endovascular aneurysm repair (EVAR) impairs recovery and quality of life, yet individualized preoperative risk tools are scarce. We conducted a retrospective dual-center cohort study of consecutive EVAR patients from Fuxing and Xuanwu Hospitals. The endpoint was new-onset postoperative buttock claudication. Missingness was quantified for each predictor and handled using complete-case analysis or model-based single imputation according to the extent of missingness. Data were split into training and held-out test sets at a 70:30 ratio with outcome stratification. Predictor screening, preprocessing, and hyperparameter tuning were performed within the training/resampling framework to minimize data leakage. Ten algorithms were tuned using stratified 10-fold cross-validation, and test set performance was assessed using discrimination, threshold-based metrics, calibration plots, calibration intercept/slope, Brier score, and decision-curve analysis. SHapley Additive exPlanations (SHAP) provided model-agnostic explanations. A web calculator was deployed. Among 272 patients, 71 (26.1%) developed claudication. Independent risk factors included aneurysm with iliac involvement (adjusted OR 4.04), male sex (3.26), unilateral (3.86) and bilateral internal iliac artery embolization (8.61), and hyperlipidemia (5.66); >2 distal internal iliac branches was protective (0.15). On the test set, the neural network achieved the highest AUROC (test ROC), with the highest sensitivity (0.810) and top F1 (0.557) at balanced specificity (0.617); CatBoost maximized accuracy (0.790) and specificity (0.900). Calibration was acceptable, and DCA showed positive net benefit across clinically plausible thresholds. SHAP confirmed physiologic directions and enabled case-level interpretation. An explainable machine learning framework accurately stratifies risk of buttock claudication after EVAR, highlighting the roles of internal iliac embolization, iliac involvement, and distal branch anatomy. The publicly available Shiny tool supports perfusion-aware planning and shared decision-making. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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17 pages, 26938 KB

Open AccessArticle

Dual-SwinOrd: A Dual-Head Swin Transformer with Semantic Prior Injection for Ordinal Diabetic Retinopathy Grading

by Wenjuan Yu, Xiaonan Si and Jingxiang Zhong

Bioengineering 2026, 13(4), 374; https://doi.org/10.3390/bioengineering13040374 - 24 Mar 2026

Cited by 3 | Viewed by 1097

Abstract

Diabetic retinopathy (DR) is the largest cause of permanent vision loss in the working-age population, making automated grading critical for timely therapeutic intervention. While recent deep learning algorithms have improved feature discrimination, modern state-of-the-art systems have two fundamental drawbacks. First, most models rely on standard Convolutional Neural Networks, which struggle to capture long-range relationships and lack semantic reasoning, resulting in visual findings that do not correlate with clinical knowledge. Second, present approaches often consider grading as a nominal classification or a pure ordinal regression task, failing to strike a compromise between high classification accuracy and severity-consistent predictions (Quadratic Weighted Kappa). To address these challenges, we propose Dual-SwinOrd, a novel framework that integrates a hierarchical Vision Transformer with a semantically guided dual-head mechanism. Specifically, we use a Swin Transformer backbone to extract hierarchical features, effectively capturing global retinal structures. To handle diverse lesion scales, we incorporate a Progressive Lesion-aware Kernel Attention (PLKA) module and a Semantic Prior Modulation (SPM) module guided by PubMedCLIP, bridging the gap between visual features and medical linguistic priors. In addition, we propose a Dual-Head learning strategy that decouples the optimization objective into two parallel streams: a Classification Head to maximize diagnostic accuracy and an Ordinal Regression Head (DPE) to enforce rank-consistency. This design effectively mitigates the trade-off between precision and ordinality. Extensive experiments on the APTOS 2019 and DDR datasets demonstrate that Dual-SwinOrd achieves state-of-the-art performance, yielding an Accuracy of 87.98% and a Quadratic Weighted Kappa (QWK) of 0.9370 on the APTOS 2019 dataset, as well as an Accuracy of 86.54% and a QWK of 0.9040 on the DDR dataset. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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24 pages, 3028 KB

Open AccessArticle

Cross-Modality Transfer Learning from PSG to FMCW Radar for Event-Level Apnea–Hypopnea Segmentation

by Saihu Lu, Peng Wang, Zhenfeng Li, Pang Wu, Xianxiang Chen, Lidong Du, Libin Jiang and Zhen Fang

Bioengineering 2026, 13(3), 283; https://doi.org/10.3390/bioengineering13030283 - 27 Feb 2026

Viewed by 1420

Abstract

Sleep apnea–hypopnea syndrome (SAHS) is a common sleep-related breathing disorder associated with substantial cardiovascular and neurocognitive risks. Although polysomnography (PSG) remains the clinical gold standard for diagnosis, its cost, operational burden, and limited accessibility hinder scalable and longitudinal home monitoring. Frequency-modulated continuous-wave (FMCW) radar provides unobtrusive, non-contact respiration sensing, yet radar-based event detection is often constrained by scarce annotations and pronounced domain shifts relative to PSG signals. In this work, we propose a deep learning framework for apnea–hypopnea event detection from FMCW radar that combines a 1D U-Net segmentation backbone with multi-head self-attention (MHSA) and cross-modality transfer learning. The model was first pre-trained on a large public PSG dataset to learn transferable respiratory-event representations and then fine-tuned on a smaller clinically annotated radar respiration dataset using synchronized PSG labels. It produced per-sample event probabilities, which were further refined via temporal post-processing to generate event-level detections and apnea–hypopnea index (AHI) estimates. Experimental results demonstrate strong performance in the radar domain, achieving precision of

0.8137 \pm 0.0332

, recall of

0.8369 \pm 0.0470

, and an F1-score of

0.8167 \pm 0.0052

. Overall, these results indicate that PSG-to-radar transfer learning enables accurate, low-cost, and non-contact sleep apnea screening, supporting scalable longitudinal monitoring in home-like settings. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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28 pages, 8658 KB

Open AccessArticle

Time–Frequency Respiratory Impedance Maps Enable Within-Breath Deep Learning for Small Airway Dysfunction Identification

by Dongfang Zhao, Sunxiaohe Li, Peng Wang, Pang Wu, Zhenfeng Li, Lidong Du, Xianxiang Chen, Ting Yang, Jingen Xia and Zhen Fang

Bioengineering 2026, 13(3), 280; https://doi.org/10.3390/bioengineering13030280 - 27 Feb 2026

Viewed by 845

Abstract

Small airway dysfunction (SAD) is an early functional abnormality associated with multiple chronic airway diseases. However, clinical assessment often relies on spirometry-based indices, which require forced maneuvers and are sensitive to subject effort, thereby increasing patient burden and complicating quality control. In contrast, Impulse Oscillometry (IOS) requires only tidal breathing, imposing minimal subject burden while providing respiratory impedance indices informative for SAD identification. This study proposes a dual-domain complementary deep learning framework based on IOS for SAD identification, leveraging within-breath impedance dynamics. Specifically, raw IOS time-series signals are transformed into time–frequency respiratory impedance maps (TFRIM) capturing impedance over frequency and within-breath time. A two-stream architecture is then used to jointly learn complementary features from TFRIM and the original time-series signals. To mitigate inter-subject baseline variability, we further introduce a demographics-driven adaptive feature modulation module for subject-specific calibration. The model jointly predicts multiple small-airway indices, with decision-level fusion applied during inference. Experimental validation on 2510 subjects using five-fold cross-validation demonstrates that the proposed framework achieves an accuracy of 81.39%, outperforming representative baselines. These results suggest the potential utility of combining within-breath IOS dynamics with subject-specific calibration for SAD identification, warranting further external validation before screening deployment. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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14 pages, 630 KB

Open AccessArticle

Disease-Specific Prediction of Missense Variant Pathogenicity with DNA Language Models and Graph Neural Networks

by Mohamed Ghadie, Sameer Sardaar and Yannis Trakadis

Bioengineering 2025, 12(10), 1098; https://doi.org/10.3390/bioengineering12101098 - 13 Oct 2025

Cited by 3 | Viewed by 2916

Abstract

Accurate prediction of the impact of genetic variants on human health is of paramount importance to clinical genetics and precision medicine. Recent machine learning (ML) studies have tried to predict variant pathogenicity with different levels of success. However, most missense variants identified on a clinical basis are still classified as variants of uncertain significance (VUS). Our approach allows for the interpretation of a variant for a specific disease and, thus, for the integration of disease-specific domain knowledge. We utilize a comprehensive knowledge graph, with 11 types of interconnected biomedical entities at diverse biomolecular and clinical levels, to classify missense variants from ClinVar. We use BioBERT to generate embeddings of biomedical features for each node in the graph, as well as DNA language models to embed variant features directly from genomic sequence. Next, we train a two-stage architecture consisting of a graph convolutional neural network to encode biological relationships. A neural network is then used as the classifier to predict disease-specific pathogenicity of variants, essentially predicting edges between variant and disease nodes. We compare performance across different versions of our model, obtaining prediction-balanced accuracies as high as 85.6% (sensitivity: 90.5%; NPV: 89.8%) and discuss how our work can inform future studies in this area. Full article

(This article belongs to the Special Issue AI-Driven Approaches to Diseases Detection and Diagnosis)

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