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Advances in the Use of Artificial Intelligence for the Diagnosis...
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Advances in the Use of Artificial Intelligence for the Diagnosis and Management of Hand Conditions

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: closed (31 May 2026) | Viewed by 3586

Special Issue Editor

Dr. Ishith Seth

E-Mail Website
Guest Editor
Faculty of Medicine, Dentistry and Health Sciences, Monash University, Melbourne, Australia
Interests: artificial intelligence; machine learning; hand conditions; preoperative diagnosis; postoperative prognosis; surgical outcomes; imaging techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) in healthcare has shown promising advancements, particularly in diagnosing and managing hand conditions. This Special Issue aims to explore cutting-edge applications of AI, including machine learning, deep learning, and computer vision, in preoperative diagnosis, surgical planning, and postoperative prognosis for hand-related pathologies. Key focus areas include AI’s role in detecting fractures, tendon injuries, nerve compressions, and conditions such as Dupuytren’s disease and arthritis. Additionally, this issue will address the use of AI to enhance imaging modalities, improve diagnostic accuracy, and predict surgical outcomes, complications, and rehabilitation trajectories. By bringing together original research, reviews, and case studies, this issue aims to highlight the potential of AI to revolutionise hand surgery and therapy while addressing challenges such as data variability, model validation, and clinical adoption.

Dr. Ishith Seth
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 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. 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

  • artificial intelligence
  • machine learning
  • hand conditions
  • preoperative diagnosis
  • postoperative prognosis
  • surgical outcomes
  • imaging techniques

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Published Papers (3 papers)

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Research

12 pages, 1563 KB  
Article
Radiograph-Based Deep Learning Model to Support Finger Joint Selection for Ultrasound Examination in Rheumatoid Arthritis
by Youngjae Park, Keum San Chun, Seungeun Lee, Joon-Yong Jung, Sungwon Lee, Hyemin Park, Tuan Dinh Le, Hyeondeok Choi and Wan-Uk Kim
Diagnostics 2026, 16(11), 1689; https://doi.org/10.3390/diagnostics16111689 - 29 May 2026
Viewed by 133
Abstract
Background/Objectives: Ultrasound is the standard imaging modality to evaluate the inflammatory changes in hand joints of rheumatoid arthritis (RA) patients. However, it is operator-dependent and takes a long time to examine. In this study, we developed a radiograph-based deep learning (DL) model to [...] Read more.
Background/Objectives: Ultrasound is the standard imaging modality to evaluate the inflammatory changes in hand joints of rheumatoid arthritis (RA) patients. However, it is operator-dependent and takes a long time to examine. In this study, we developed a radiograph-based deep learning (DL) model to support prioritization of finger joints for ultrasound (US) examination in RA patients. Methods: In this retrospective study, hand radiographs from RA patients who underwent same-day US examination of bilateral finger joints were analyzed. A DL model was developed using hand radiographs from 270 patients (2043 finger joints) to estimate joint-level likelihood of inflammatory activity. US findings served as the reference standard for model training, while clinical findings of joint tenderness and swelling were incorporated as additional tabular inputs. Model performance was evaluated in a temporal-split test cohort consisting of 40 patients (270 joints) and compared with the performance of a clinical-only logistic regression model based on joint tenderness and swelling. Results: In the test set, the DL model demonstrated higher sensitivity (82.1% vs. 38.5%), negative predictive value (96.8% vs. 90.3%), and F1-score (69.6% vs. 48.4%) than the clinical-only model. Although the area under the receiver operating characteristic curve did not differ significantly between models (p = 0.43), precision–recall (PR) analysis showed superior performance of the DL model, with a higher area under the PR curve (0.625 vs. 0.540). At the threshold maximizing the F1-score, DL-assisted triage reduced the number of finger joints selected for US examination by approximately 80%. Conclusions: A radiograph-based DL model can support efficient prioritization of finger joints for US examination in RA, offering a practical approach to enhance joint-level US triage in routine clinical practice. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence for the Diagnosis and Management of Hand Conditions)
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37 pages, 8151 KB  
Article
Explainable Ensemble Learning for Robust Severity Stratification of Carpal Tunnel Syndrome from Clinical Data
by Muhammet Emin Sahin, Hasan Ulutas, Murat Korkmaz, Mucella Ozbay Karakus, Orhan Er and Huriye Unluel
Diagnostics 2026, 16(11), 1604; https://doi.org/10.3390/diagnostics16111604 - 25 May 2026
Viewed by 244
Abstract
Background/Objectives: This paper aims to design an explainable and accurate ML framework to support the automatic classification of Carpal Tunnel Syndrome (CTS) severity from structured patient data. Methods: For the experiment, an open-source dataset of 1037 samples was used. Following stratified partitioning, 305 [...] Read more.
Background/Objectives: This paper aims to design an explainable and accurate ML framework to support the automatic classification of Carpal Tunnel Syndrome (CTS) severity from structured patient data. Methods: For the experiment, an open-source dataset of 1037 samples was used. Following stratified partitioning, 305 samples were held out as the test set; the remaining training set (n = 732) was augmented to 1216 balanced samples via ADASYN, yielding an 80/20 train/test ratio relative to the final dataset (n = 1521). In order to solve the problem of imbalance associated with CTS cases of moderate and severe severity, the Adaptive Synthetic Sampling (ADASYN) technique was employed. The model’s predictive capacity was increased by means of feature engineering methods, such as polynomial transformations and clinically relevant interactions. Specifically, four ensemble learning models (XGBoost, Random Forest, LightGBM, and CatBoost) were optimized and ensembled with the use of a stacking approach with a base algorithm of LightGBM. The explainability of the model was ensured through SHAP and LIME analysis. Results: As a result, the stacking ensemble was able to reach a test accuracy of 91.15%, an F1-score of 91.13%, and an ROC-AUC of 0.9708. The proposed ensemble performed superiorly compared to any other individual algorithm while having stable performance across all severity categories. Conclusions: Through the explainability analysis, it was observed that such a classification model relies on important clinically relevant predictors, including cross-sectional area (CSA), duration of symptoms, pain level measured by the numeric rating scale of pain (NRS), and palmar bowing (PB). Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence for the Diagnosis and Management of Hand Conditions)
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17 pages, 220 KB  
Article
Management of Dupuytren’s Disease: A Multi-Centric Comparative Analysis Between Experienced Hand Surgeons Versus Artificial Intelligence
by Ishith Seth, Gianluca Marcaccini, Kaiyang Lim, Marco Castrechini, Roberto Cuomo, Sally Kiu-Huen Ng, Richard J. Ross and Warren M. Rozen
Diagnostics 2025, 15(5), 587; https://doi.org/10.3390/diagnostics15050587 - 28 Feb 2025
Cited by 19 | Viewed by 2571
Abstract
Background: Dupuytren’s fibroproliferative disease affecting the hand’s palmar fascia leads to progressive finger contractures and functional limitations. Management of this condition relies heavily on the expertise of hand surgeons, who tailor interventions based on clinical assessment. With the growing interest in artificial [...] Read more.
Background: Dupuytren’s fibroproliferative disease affecting the hand’s palmar fascia leads to progressive finger contractures and functional limitations. Management of this condition relies heavily on the expertise of hand surgeons, who tailor interventions based on clinical assessment. With the growing interest in artificial intelligence (AI) in medical decision-making, this study aims to evaluate the feasibility of integrating AI into the clinical management of Dupuytren’s disease by comparing AI-generated recommendations with those of expert hand surgeons. Methods: This multicentric comparative study involved three experienced hand surgeons and five AI systems (ChatGPT, Gemini, Perplexity, DeepSeek, and Copilot). Twenty-two standardized clinical prompts representing various Dupuytren’s disease scenarios were used to assess decision-making. Surgeons and AI systems provided management recommendations, which were analyzed for concordance, rationale, and predicted outcomes. Key metrics included union accuracy, surgeon agreement, precision, recall, and F1 scores. The study also evaluated AI performance in unanimous versus non-unanimous cases and inter-AI agreements. Results: Gemini and ChatGPT demonstrated the highest union accuracy (86.4% and 81.8%, respectively), while Copilot showed the lowest (40.9%). Surgeon agreement was highest for Gemini (45.5%) and ChatGPT (42.4%). AI systems performed better in unanimous cases (accuracy up to 92.0%) than in non-unanimous cases (accuracy as low as 35.0%). Inter-AI agreements ranged from 75.0% (ChatGPT-Gemini) to 48.0% (DeepSeek-Copilot). Precision, recall, and F1 scores were consistently higher for ChatGPT and Gemini than for other systems. Conclusions: AI systems, particularly Gemini and ChatGPT, show promise in aligning with expert surgical recommendations, especially in straightforward cases. However, significant variability exists, particularly in complex scenarios. AI should be viewed as complementary to clinical judgment, requiring further refinement and validation for integration into clinical practice. Full article
(This article belongs to the Special Issue Advances in the Use of Artificial Intelligence for the Diagnosis and Management of Hand Conditions)
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