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Bioengineering
Special Issues
AI-Driven Approaches to Diseases Detection and Diagnosis
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AI-Driven Approaches to Diseases Detection and Diagnosis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2431

Special Issue Editors

Dr. Yannis Trakadis

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Guest Editor
Department of Medical Genetics, McGill University Health Centre, Montreal, QC H3H 1P3, Canada
Interests: medical genetics; precision medicine; clinical integration of omics & AI tools
Dr. Antonio Brunetti

E-Mail Website
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

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • 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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Published Papers (3 papers)

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Research

24 pages, 3028 KB  
Article
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 140
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) [...] Read more.
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±0.0332, recall of 0.8369±0.0470, and an F1-score of 0.8167±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  
Article
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 142
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, [...] Read more.
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  
Article
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
Viewed by 1841
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 [...] Read more.
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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