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Diagnostics
Special Issues
Applications of Artificial Intelligence in Orthopedics
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Applications of Artificial Intelligence in Orthopedics

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 October 2025 | Viewed by 449

Special Issue Editor

Dr. Zhonghai Li

E-Mail Website
Guest Editor
Orthopedic Department, First Affiliated Hospital of Dalian Medical University, Dalian, China
Interests: spine; orthopedics; surgery; digital twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses specifically on the role of AI in advancing medical diagnosis within the orthopedic field. It explores how AI technologies are being utilized to enhance the accuracy and efficiency of diagnostic processes, ultimately leading to better patient outcomes.

Key areas of emphasis include AI-driven image analysis techniques that automatically detect and classify orthopedic abnormalities in imaging studies such as X-rays, MRIs, and CT scans. These technologies transform the diagnostic landscape by providing clinicians with rapid, reliable, and consistent results. Additionally, the special issue delves into predictive modeling using AI algorithms, which can predict disease progression and patient outcomes based on individual patient data. This information can be invaluable in guiding treatment decisions and ensuring that patients receive the most appropriate care.

Dr. Zhonghai Li
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

  • artificial intelligence
  • orthopedics
  • medical diagnosis
  • image analysis
  • predictive modeling

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Published Papers (1 paper)

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Research

13 pages, 830 KiB  
Article
Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery
by Humam Baki and İsmail Bülent Özçelik
Diagnostics 2025, 15(14), 1787; https://doi.org/10.3390/diagnostics15141787 - 16 Jul 2025
Viewed by 273
Abstract
Background/Objectives: Postoperative deep vein thrombosis (DVT) is a common and serious complication after tibial fracture surgery. This study aimed to develop and evaluate machine learning (ML) models to predict the occurrence of DVT following tibia fracture surgery. Methods: A retrospective analysis [...] Read more.
Background/Objectives: Postoperative deep vein thrombosis (DVT) is a common and serious complication after tibial fracture surgery. This study aimed to develop and evaluate machine learning (ML) models to predict the occurrence of DVT following tibia fracture surgery. Methods: A retrospective analysis was conducted on patients who had undergone surgery for isolated tibial fractures. A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. Model performance was assessed based on discrimination, quantified by the area under the receiver operating characteristic curve (AUC-ROC), and calibration, measured using Brier scores, with internal validation performed via bootstrapping. Results: Of 471 patients, 80 (17.0%) developed postoperative DVT. The ML models achieved high overall accuracy in predicting DVT. Twenty-four models showed similarly excellent discrimination (pairwise AUC comparisons, p > 0.05). The top-performing model (random forest with RFE) attained an AUC of ~0.99, while several others (including LightGBM and SVM-based models) also reached AUC values in the 0.97–0.99 range. Notably, support vector machine models paired with Boruta or LASSO feature selection demonstrated the best calibration (lowest Brier scores), indicating reliable risk estimation. The final selected SVM models achieved high specificity (≥95%) with moderate sensitivity (~75–80%) for DVT detection. Conclusions: ML models demonstrated high accuracy in predicting postoperative DVT following tibial fracture surgery. Support vector machine-based models showed particularly favorable discrimination and calibration. These results suggest the potential utility of ML-based risk stratification to guide individualized prophylaxis, warranting further validation in prospective clinical settings. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Orthopedics)
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