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Diagnostics
Special Issues
New Technologies and Tools Used for Risk Assessment of Diseases
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New Technologies and Tools Used for Risk Assessment of Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 996

Special Issue Editor

Dr. Chih-Wei Huang

E-Mail Website
Guest Editor
1. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
2. International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
Interests: digital health technologies; artificial intelligence (AI); big data; risk assessment; personalized healthcare; chronic disease management; health IoT technologies for real-time health monitoring; precision medicine

Special Issue Information

Dear Colleagues,

With the rapid advancements in artificial intelligence (AI), big data analytics, wearable technology, and digital health solutions, new methodologies for disease risk assessment are emerging. These technologies enhance early diagnosis, risk stratification, and personalized intervention strategies, improving patient outcomes and reducing healthcare burdens.

This Special Issue aims to highlight recent developments in innovative technologies and computational tools used for disease risk assessment across various medical domains. We invite original research, reviews, and case studies that explore the integration of AI, machine learning, medical imaging, wearable devices, and digital biomarkers in predicting and managing disease risks.

Potential topics of interest include, but are not limited to:

  • AI and machine learning models for disease risk prediction
  • Digital biomarkers and wearable sensor technologies
  • Big data analytics in epidemiology and risk assessment
  • Multi-modal data fusion for personalized risk stratification
  • Computational modeling for early disease detection
  • Explainable AI in medical risk prediction
  • Telemedicine and mobile health applications for risk assessment
  • Ethical and regulatory considerations in AI-driven risk assessment

We welcome contributions from researchers, clinicians, and industry experts to showcase innovative approaches that bridge the gap between technology and clinical practice.

Dr. Chih-Wei Huang
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 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. Diagnostics 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 2600 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

  • digital health
  • AI in healthcare
  • machine learning
  • disease risk prediction
  • medical imaging
  • wearable technology
  • big data analytics
  • telemedicine
  • personalized medicine
  • explainable AI

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

Published Papers (2 papers)

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Research

15 pages, 3893 KiB  
Article
Exploration of 3D Few-Shot Learning Techniques for Classification of Knee Joint Injuries on MR Images
by Vinh Hiep Dang, Minh Tri Nguyen, Ngoc Hoang Le, Thuan Phat Nguyen, Quoc-Viet Tran, Tan Ha Mai, Vu Pham Thao Vy, Truong Nguyen Khanh Hung, Ching-Yu Lee, Ching-Li Tseng, Nguyen Quoc Khanh Le and Phung-Anh Nguyen
Diagnostics 2025, 15(14), 1808; https://doi.org/10.3390/diagnostics15141808 - 18 Jul 2025
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Abstract
Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning [...] Read more.
Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning (FSL) addresses this by enabling models to classify new conditions from minimal annotated examples, often leveraging knowledge from related tasks. However, creating robust 3D FSL frameworks for varied knee injuries remains challenging. Methods: We introduce MedNet-FS, a 3D FSL framework that effectively classifies knee injuries by utilizing domain-specific pre-trained weights and generalized end-to-end (GE2E) loss for discriminative embeddings. Results: MedNet-FS, with knee-MRI-specific pre-training, significantly outperformed models using generic or other medical pre-trained weights and approached supervised learning performance on internal datasets with limited samples (e.g., achieving an area under the curve (AUC) of 0.76 for ACL tear classification with k = 40 support samples on the MRNet dataset). External validation on the KneeMRI dataset revealed challenges in classifying partially torn ACL (AUC up to 0.58) but demonstrated promising performance for distinguishing intact versus fully ruptured ACLs (AUC 0.62 with k = 40). Conclusions: These findings demonstrate that tailored FSL strategies can substantially reduce data dependency in developing specialized medical imaging tools. This approach fosters rapid AI tool development for knee injuries and offers a scalable solution for data scarcity in other medical imaging domains, potentially democratizing AI-assisted diagnostics, particularly for rare conditions or in resource-limited settings. Full article
(This article belongs to the Special Issue New Technologies and Tools Used for Risk Assessment of Diseases)
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13 pages, 947 KiB  
Article
Potential Protective Effect of Hepatitis B Immunity Against Diabetes Mellitus: A Retrospective Propensity-Matched Cohort Study
by Nhu Quynh Phan, Shih-Jung Lin, Ngoc Hoang Le, Van Thuan Nguyen, Tan Ha Mai, Jin-Hua Chen, Min-Huei Hsu, Dinh Khanh Hoang, Phung Manh Thang, Ya-Li Huang and Chiehfeng Chen
Diagnostics 2025, 15(13), 1610; https://doi.org/10.3390/diagnostics15131610 - 25 Jun 2025
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Abstract
Background: Hepatitis B virus (HBV) infection affects glucose metabolism and increases diabetes risk; HBV vaccination may reduce this risk. The role of HBV immunity in diabetes prevention among individuals without HBV infection is underexplored. This study aims to evaluate whether HBV immunity [...] Read more.
Background: Hepatitis B virus (HBV) infection affects glucose metabolism and increases diabetes risk; HBV vaccination may reduce this risk. The role of HBV immunity in diabetes prevention among individuals without HBV infection is underexplored. This study aims to evaluate whether HBV immunity reduces diabetes risk in individuals without HBV infection. Methods: This retrospective cohort study used de-identified electronic medical records from TriNetX. Adults with hepatitis B surface antibody (HBsAb) results without a history of HBV infection or diabetes were identified. Diabetes was defined on the basis of a diabetes diagnosis, diabetes medication use, or glycated hemoglobin ≥ 6.5%. Propensity score matching was conducted to balance demographics and comorbidities between groups. Results: The HBV-immunized group had a 15% lower diabetes risk than the HBV-unimmunized group (HR: 0.85 [0.84–0.87]). A dose–response effect was observed, with higher HBsAb levels showing a greater reduction in the risk of diabetes. HBsAb levels of ≥100 and ≥1000 mIU/mL were associated with 19% (HR: 0.81 [0.80–0.83]) and 43% (HR: 0.57 [0.54–0.60]) reductions in diabetes risk, respectively, compared with HBsAb < 10 mIU/mL. The reduced risk of diabetes was associated with age. Immunized individuals aged 18 to 44 years, 45 to 64 years, and ≥65 years had 20% (HR: 0.80 [0.78–0.82]), 11% (HR: 0.89 [0.87–0.92]), and 12% (HR: 0.88 [0.84–0.91]) lower diabetes risks, respectively, compared with unimmunized individuals. Conclusions: HBV immunity may be associated with a reduced risk of diabetes, suggesting broader HBV vaccination as a dual-benefit strategy for the prevention of hepatitis B and diabetes, especially in regions with a high prevalence of both diseases. Full article
(This article belongs to the Special Issue New Technologies and Tools Used for Risk Assessment of Diseases)
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