A parallel framework correlating AI radiographic diagnosis with computational biomechanics for tibial plateau fractures in wushu athletes
Xiaoqing Xie, Bingsi Xi, Yan Kuang, Pingping Dong, Jing ChenAbstract
Objectives
Tibial plateau fractures (TPFs) are serious knee injuries frequently associated with high-impact sports such as Wushu. This study proposes a parallel analytical framework that integrates artificial intelligence (AI)-based radiographic diagnosis with computational biomechanics to investigate the risk factors and injury mechanisms of TPFs during the Wushu Tornado Kick (Xuanfengjiao).
Methods
An EfficientNet-B3 deep learning model was trained on MURA and Kaggle datasets for automated radiographic detection of TPFs, achieving high diagnostic accuracy. Concurrently, 3D pose estimation and spatial kinematics were utilized to extract biomechanical parameters, specifically Knee Flexion Angle (KFA) and vertical Ground Reaction Forces (GRF), from elite Wushu athletes.
Results
The kinematic data were mapped to predict structural stresses on the tibial plateau upon landing impact. Our dual-pathway approach demonstrates that increased vertical GRF combined with suboptimal KFA significantly elevates the risk of specific Schatzker-type fractures. The AI model successfully correlated these mechanically predicted injury patterns with radiographic evidence.
Conclusions
This framework bridges the gap between predictive sports biomechanics and clinical radiographic diagnosis, offering a robust tool for injury prevention and targeted rehabilitation in Wushu athletes.