Non-Imaging Differential Diagnosis of Lower Limb Osteoarthritis: An Interpretable Machine Learning Framework
Baigarayeva Z. Boltaboyeva A. Imanbek B. Amangeldy B. Tasmurzayev N. Ozhikenov K. Ozhiken A. Alimbayeva Z. Maeda-Nishino N.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Algorithms
2026#19Issue 1
Background: Osteoarthritis (OA) is a prevalent chronic degenerative disorder, with coxarthrosis (hip OA) and gonarthrosis (knee OA) representing its most significant clinical manifestations. While diagnosis typically relies on imaging, such methods can be resource-intensive and insensitive to early disease trajectories. Objective: This study aims to achieve the differential diagnosis of coxarthrosis and gonarthrosis using solely routine preoperative clinical and laboratory data, benchmarking state-of-the-art machine learning algorithms. Methods: A retrospective analysis was conducted on 893 patients (617 with knee OA, 276 with hip OA) from a clinical hospital in Almaty, Kazakhstan. The study evaluated a diverse portfolio of models, including gradient boosting decision trees (LightGBM, XGBoost, CatBoost), deep learning architectures (RealMLP, TabDPT, TabM), and the pretrained tabular foundation model RealTabPFN v2.5. Results: The RealTabPFN v2.5 (Tuned) model achieved superior performance, recording a mean ROC–AUC of 0.9831, accuracy of 0.9485, and an F1-score of 0.9474. SHAP interpretability analysis identified heart rate (66.2%) and age (18.1%) as the dominant predictors driving the model’s decision-making process. Conclusion: Pretrained tabular foundation models demonstrate exceptional capability in distinguishing OA subtypes using limited clinical datasets, outperforming traditional ensemble methods. This approach offers a practical, high-performance triage tool for primary clinical assessment in resource-constrained settings.
clinical decision support , coxarthrosis , differential diagnosis , explainable AI , gonarthrosis , machine learning , osteoarthritis , tabular foundation models
Text of the article Перейти на текст статьи
Institute of Automation and Information Technology, Satbayev University, Almaty, 050013, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP Kazakhstan R&D Solutions, Almaty, 050056, Kazakhstan
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, 94305, CA, United States
HAKUAI Medical Corporation, Osaka, 573-1010, Japan
Institute of Automation and Information Technology
Faculty of Information Technologies and Artificial Intelligence
LLP Kazakhstan R&D Solutions
Department of Psychiatry and Behavioral Sciences
HAKUAI Medical Corporation
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026