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Bioengineering
Special Issues
Machine Learning in Chronic Diseases
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Machine Learning in Chronic Diseases

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2010

Special Issue Editors

Dr. Habibollah (Habib) Pirnejad

E-Mail Website
Guest Editor
Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
Interests: medical/health informatics; clinical decision support
Dr. Amin Naemi

E-Mail Website
Guest Editor
Department of Biology, University of Southern Denmark, 5230 Odense, Denmark
Interests: machine learning; predictive model

Special Issue Information

Dear Colleagues,

Chronic diseases, such as diabetes, cardiovascular disease, cancer, and dementia, are on the rise, posing a significant global health burden. These conditions are increasingly prevalent, complex, and costly to manage, straining healthcare systems worldwide. Despite the growing challenge, global efforts to address chronic diseases remain inadequate.

Machine learning (ML) and artificial intelligence (AI) offer promising solutions to these challenges. From primary prevention to tertiary care, these technologies have the capacity to enhance healthcare delivery across the continuum of care. ML algorithms can facilitate early detection, enable personalized interventions, optimize clinical workflows, and inform policy decisions by uncovering insights from vast and complex datasets. However, translating these advancements into real-world healthcare applications presents a range of challenges, including data integration, model interpretability, ethical concerns, and scalability.

This Special Issue invites researchers to contribute innovative studies that explore the application of ML in various aspects of chronic disease management. We welcome submissions that address the following:

  • Early detection and risk stratification.
  • Patient monitoring and personalized care.
  • System-wide optimization and resource allocation.
  • Ethical considerations and fairness in ML applications.
  • Integration of ML into real-world healthcare workflows.

Join a global community of experts dedicated to transforming healthcare by submitting your work to this Special Issue. Your research can inspire innovation, drive change, and ultimately improve the lives of countless individuals affected by chronic diseases. We encourage you to contribute your valuable insights and help shape the future of healthcare.

Dr. Habibollah (Habib) Pirnejad
Dr. Amin Naemi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • machine learning
  • chronic diseases
  • artificial intelligence
  • healthcare
  • personalized medicine
  • ethical AI
  • predictive analytics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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25 pages, 4241 KB  
Article
Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions
by Mohammad Mahdi Aghabeigi Alooghareh, Mohammad Mohsen Sheikhey, Ali Sahafi, Habibollah Pirnejad and Amin Naemi
Bioengineering 2025, 12(8), 840; https://doi.org/10.3390/bioengineering12080840 - 3 Aug 2025
Viewed by 1490
Abstract
The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and [...] Read more.
The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article
(This article belongs to the Special Issue Machine Learning in Chronic Diseases)
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