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Diagnostics
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Advances in Disease Prediction—2nd Edition
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Advances in Disease Prediction—2nd Edition

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 2375

Editors

Prof. Dr. Chi-Chang Chang

E-Mail Website
Guest Editor
Chair of Medical Informatics Department, Chung Shan Medical University, Taichung City, Taiwan
Interests: medical informatics; clinical decision analysis; simulation modeling; shared medical decision making
Special Issues, Collections and Topics in MDPI journals
Dr. Chalong Cheewakriangkrai

E-Mail Website
Guest Editor
Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: clinical epidemiology; chemotherapy and targeted therapy; surgery; gynecologic oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of digital health, complex and ubiquitous health data are collected from various sources, such as electronic health records, medical monitoring devices, wearable health systems, and mobile phone applications. Big data analytics techniques such as statistical analysis, machine learning, deep learning, generative intelligence, and digital twins can be applied to build innovative advances in disease prediction. Recently, the need for automated/intelligent laboratory recommendation systems has increased to provide more accurate and faster diagnoses. The fusion of various clinical data sources promoted by advances in laboratory approaches can significantly improve the diagnosis of diseases, illustrating how the digital revolution transforms clinical diagnostic practice. Based on concrete evidence, it is crucial and has a significant impact on the implementation of health care and programs. This fact highlights the important role of early diagnostics, including disease control, a range of treatment options, improved health services, improved health disparities, quality of life, etc. The aim of this Special Issue is to provide a comprehensive and current collection of state-of-the-art studies to advance disease prediction. Practical experience and experiments on the above-mentioned innovative analysis issues are also welcomed.

You can also see the published papers of the first edition of this Special Issue through the following link: https://www.mdpi.com/journal/diagnostics/special_issues/7GIT1635TL.

Prof. Dr. Chi-Chang Chang
Dr. Chalong Cheewakriangkrai
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-anonymized 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

  • early diagnostics
  • medical big data analytics
  • intelligent diagnostic models in public health
  • survival analysis and health hazard evaluations
  • machine learning and generative intelligence
  • intelligent digital twins
  • automated/intelligent laboratory recommendation systems

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

Published Papers (4 papers)

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Research

19 pages, 977 KB  
Article
Deep Learning-Based Parkinson’s Disease Classification Using RGB Plantar Pressure Gait Images: A Comparative Study of CNN and Transformer Architectures
by Chun-Yu Li, Yu-Wen Hung and Jia-Lang Xu
Diagnostics 2026, 16(18), 3021; https://doi.org/10.3390/diagnostics16183021 - 17 Sep 2026
Viewed by 72
Abstract
Background/Objectives: Parkinson’s disease (PD) is associated with gait abnormalities that provide quantitative information on motor function. This study developed and evaluated a signal-to-image deep learning framework for distinguishing participants with diagnosed PD from healthy controls using plantar pressure gait signals. Methods: [...] Read more.
Background/Objectives: Parkinson’s disease (PD) is associated with gait abnormalities that provide quantitative information on motor function. This study developed and evaluated a signal-to-image deep learning framework for distinguishing participants with diagnosed PD from healthy controls using plantar pressure gait signals. Methods: VGRF signals from the PhysioNet Gait in Parkinson’s Disease Database were transformed into RGB images encoding left-foot pressure, right-foot pressure, and the absolute bilateral difference. ResNet50, EfficientNet-B0, ViT-Tiny, and Swin-Tiny were evaluated at three resolutions and compared with 1D-CNN and BiLSTM baselines. Repeated subject-level five-fold cross-validation with three repetitions and participant-level bootstrap analysis were performed. Results: Among RGB models, ViT-Tiny at 384 × 384 achieved a mean accuracy of 77.64%, F1-score of 82.82%, and AUC of 86.40%. In the Swin-Tiny 384 × 384 ablation, the absolute bilateral-difference representation achieved the highest mean AUC (86.68%). However, its participant-level AUC advantage over RGB was not statistically conclusive (ΔAUC = 0.030, 95% CI: −0.011 to 0.068). Conclusions: Signal-derived gait images provide a feasible approach for PD classification, with bilateral-difference information showing potential discriminative value. Full article
(This article belongs to the Special Issue Advances in Disease Prediction—2nd Edition)
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16 pages, 1370 KB  
Article
CPM-XNet: Annotation-Efficient Deep-Learning Framework for Detecting Tuberculosis in Chest X-Ray Images
by Tzu-Chin Yang, Bing-Yen Wang, Jin-Yu Li, Yu-Kang Chang, Shih-Huan Lin, Chi-Chang Chang and Yen-Wei Chu
Diagnostics 2026, 16(13), 1947; https://doi.org/10.3390/diagnostics16131947 - 23 Jun 2026
Viewed by 474
Abstract
Background/Objectives: Chest X-ray (CXR) images are a widely used first-line screening tool for pulmonary tuberculosis (TB) detection but are difficult to interpret, which has increased demand for an automated screening tool. Deep-learning-based computer-aided diagnosis systems have demonstrated a classification performance comparable to [...] Read more.
Background/Objectives: Chest X-ray (CXR) images are a widely used first-line screening tool for pulmonary tuberculosis (TB) detection but are difficult to interpret, which has increased demand for an automated screening tool. Deep-learning-based computer-aided diagnosis systems have demonstrated a classification performance comparable to that of trained radiologists, but they rely on dense annotations such as lesion-level or pixel-level labels, which are costly and difficult to obtain in routine clinical workflows. We developed CPM-XNet, an annotation-efficient framework for lesion-annotation-free downstream TB classification in CXR images. Methods: CPM-XNet incorporates a compressing–projecting mask (CPM) to provide soft lung-aware modulation while preserving global contextual information. The CPM-modulated images are then used for downstream classification with multiple convolutional neural network backbones and a vision transformer baseline. Results: Experiments were conducted using an internal hospital dataset and public TB datasets, and CPM-XNet showed improved performance compared with baseline models trained on unmodulated images. In a repeated-seed evaluation of the main ResNet-101 configuration on the Tung cohort, CPM-ResNet101 showed higher and more stable performance than the non-CPM counterpart and demonstrated significant paired improvement using McNemar’s exact test. An ablation analysis indicated that CPM modulation was the main contributor to performance improvement while data augmentation and the classifier architecture further influenced the overall robustness. Conclusions: CPM-XNet provides an annotation-efficient strategy for lesion-annotation-free downstream TB classification in CXR images. The findings support preliminary technical feasibility, although larger, naturally imbalanced, cross-institutional validation is required before clinical deployment can be inferred. Full article
(This article belongs to the Special Issue Advances in Disease Prediction—2nd Edition)
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15 pages, 1929 KB  
Article
Prediction of Surgical Intervention in Acute Knee Trauma: A Focus on Threshold-Specific Performance and Clinical Decision Utility
by Eun Byeol Choe, Joungeun Lee, Won-Kee Choi, Young Woo Seo and Sang Gyu Kwak
Diagnostics 2026, 16(11), 1578; https://doi.org/10.3390/diagnostics16111578 - 22 May 2026
Viewed by 449
Abstract
Background: Acute knee trauma is a common reason for emergency department visits, yet early identification of patients requiring surgical intervention remains challenging. Most existing prediction studies focus on discrimination metrics and provide limited guidance for clinical decision-making. Methods: We conducted a [...] Read more.
Background: Acute knee trauma is a common reason for emergency department visits, yet early identification of patients requiring surgical intervention remains challenging. Most existing prediction studies focus on discrimination metrics and provide limited guidance for clinical decision-making. Methods: We conducted a retrospective study of 905 patients presenting to the emergency department with acute knee trauma. Prediction models were developed using logistic regression, random forest, and extreme gradient boosting (XGBoost) based on routinely available clinical variables. Model performance was evaluated in terms of discrimination (AUROC, AUPRC), calibration, and clinical utility. Threshold-specific performance metrics and decision curve analysis were used to assess clinical applicability, and patients were stratified into risk groups based on predicted probabilities. Results: Among 905 patients, 163 (18.0%) underwent surgical intervention. Logistic regression and random forest demonstrated comparable performance (AUROC 0.748 and 0.744, respectively), whereas XGBoost showed lower discrimination (AUROC 0.632). Calibration was acceptable overall but less stable at higher predicted probabilities. Threshold-specific analysis demonstrated meaningful trade-offs between sensitivity and specificity across probability thresholds. Decision curve analysis showed that the model provided greater net benefit than default strategies within a threshold range of approximately 0.05–0.25. Risk stratification showed increasing surgical rates across risk groups, although the degree of separation was modest. Conclusions: Prediction models based on routinely available clinical variables can support early risk assessment in acute knee trauma. Their clinical usefulness depends on threshold-specific evaluation and decision-analytic approaches rather than overall performance metrics alone. These findings highlight the importance of interpreting prediction models within a clinical decision-making framework to facilitate real-world application. Full article
(This article belongs to the Special Issue Advances in Disease Prediction—2nd Edition)
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36 pages, 3288 KB  
Article
Predicting Hungry Bone Syndrome with Interpretable Machine Learning: A Single-Center Cohort of Dialysis Patients Undergoing Parathyroidectomy
by Adelina Baloi, Dorel Sandesc, Talida Georgiana Cut, Radu Caprariu, Dorin Novacescu, Cristina-Stefania Dumitru, Alina Cristina Barb, Raluca Dumache, Pavel Banov, Victoria Birlutiu, Voichita Elena Lazureanu and Flavia Zara
Diagnostics 2026, 16(10), 1469; https://doi.org/10.3390/diagnostics16101469 - 12 May 2026
Viewed by 658
Abstract
Background/Objectives: Hungry bone syndrome (HBS) is a frequent and potentially life-threatening complication following parathyroidectomy (PTX) for secondary hyperparathyroidism (SHPT) in dialysis patients, yet existing prediction tools offer limited discriminative accuracy. This study aimed to develop and internally validate an interpretable machine learning [...] Read more.
Background/Objectives: Hungry bone syndrome (HBS) is a frequent and potentially life-threatening complication following parathyroidectomy (PTX) for secondary hyperparathyroidism (SHPT) in dialysis patients, yet existing prediction tools offer limited discriminative accuracy. This study aimed to develop and internally validate an interpretable machine learning (ML) framework for preoperative HBS prediction and to derive a pragmatic bedside risk score from ML-derived feature importance. Methods: Ninety end-stage renal disease patients who underwent PTX for drug-refractory SHPT at a single center (2019–2023) were analyzed. Eight supervised ML classifiers were trained on 24 preoperative features (19 raw variables plus 5 engineered features) and evaluated under 5-fold stratified cross-validation repeated 10 times. SHapley Additive exPlanations (SHAP) analysis was applied for model interpretability, and a composite bedside risk score was constructed from SHAP-derived feature rankings. Results: HBS occurred in 41 patients (45.6%). Random forest achieved the numerically highest discrimination among multi-feature models (AUC = 0.933 ± 0.065), outperforming previously published models, though univariate alkaline phosphatase (ALP) alone achieved a comparable cross-validated AUC of 0.958. ALP overwhelmingly dominated all predictors (mean |SHAP| = 3.37, exceeding the next-ranked feature by approximately 6.5-fold). Partial dependence analysis revealed a sigmoid-shaped ALP–HBS relationship with a critical inflection zone between 250–350 U/L, and SHAP dependence plots demonstrated that total parathyroidectomy amplifies ALP-mediated risk. A SHAP-guided composite bedside risk score (range 0–9) achieved an AUC of 0.883, with observed HBS rates rising monotonically from 0% (score 0) to 100% (score ≥ 6). Decision-curve analysis showed that univariate ALP and the multi-feature pipeline yielded comparable net benefit, with ALP preferable in the high-sensitivity regime and the multi-feature model preferable at high-specificity thresholds; net reclassification improvement was negative for the multi-feature model vs. univariate ALP, supporting the framework’s role as an interpretive rather than discriminative advance. Conclusions: An interpretable ML framework substantially improves HBS prediction over conventional models, confirms ALP as the overwhelmingly dominant predictor through a nonlinear dose–response relationship, and yields a clinically interpretable bedside risk score that, pending external validation, may support preoperative risk stratification. Full article
(This article belongs to the Special Issue Advances in Disease Prediction—2nd Edition)
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