Artificial intelligence for predicting the efficacy of Tuina in patients with knee osteoarthritis

Background Knee osteoarthritis (KOA), a prevalent condition impacting middle-aged and older adults' quality of life, is increasing globally. Tuina, a Traditional Chinese Medicine technique, shows efficacy in reducing KOA pain and improving function, but response varies. This study aimed to develop a supervised machine learning classifier to predict Tuina efficacy for KOA, aiding personalized treatment planning. Methods This retrospective, registry-based, single-center prognostic study enrolled 355 KOA patients from the Tuina Department at Yueyang Hospital (Shanghai, China) between February 1, 2016, and December 31, 2023. All received standardized Tuina therapy (20-min sessions, 5×/week for 2 weeks). Efficacy was assessed via ΔVAS (post-treatment minus baseline VAS), categorized as high (ΔVAS = 4–6) or low efficacy (ΔVAS = 0–3). Eight machine learning models (e.g., Random Forest, SVM) were trained using 80% of the data (demographics, medical history, imaging assessments, physical exam findings, baseline VAS) to predict efficacy, validated on the remaining 20%. Statistical analysis used T-tests and Chi-square tests; model performance was evaluated via F1-score and AUC. Data analysis was performed from January 2024 to March 2025. Results The average reduction in VAS scores was 3.74. Among the eight trained machine learning models, the Random Forest-based model achieved the best predictive performance for the efficacy of Tuina treatment. The top six features influencing the model included the grinding test, knee joint range of motion, body mass index (BMI), height, imaging examination, and disease course. Conclusions Artificial intelligence models can reliably predict the efficacy of Tuina therapy in KOA patients. This study provides a valuable reference for Tuina practitioners in scientifically evaluating the effectiveness of KOA treatments. Trial registration: This retrospective study was approved by the Ethics Committee of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, affiliated with Shanghai University of Traditional Chinese Medicine (No.2025-056).