A Geometric Deep Learning Model for Real-Time Prediction of Knee Joint Biomechanics Under Meniscal Extrusion

Abstract

Meniscal extrusion (ME) has been identified as a key factor contributing to knee joint dysfunction and osteoarthritis progression. Traditional finite element analysis (FEA) methods, while accurate, are computationally expensive and time-consuming, limiting their application for real-time clinical assessments and large-scale studies. This study proposes a geometric deep learning (GDL) model to predict the biomechanical responses of knee joint soft tissues, specifically focusing on the effects of varying degrees of meniscal extrusion. The model, trained on finite element analysis (FEA)-derived data and leveraging advanced AI algorithms, significantly reduces computational time while maintaining high prediction accuracy. Validation against FEA results demonstrated that the GDL model reliably predicts stress and displacement distributions, with key performance metrics including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Percent Error at Peak Location (PEatPEAK), and Percent Error in Peak Value (PEinPEAK). Compared to conventional FEA workflows, the GDL model eliminates time-consuming preprocessing steps, enabling real-time or near-real-time biomechanical assessments. This innovation provides rapid insights into knee joint mechanics, facilitating clinical decision-making, surgical planning, and personalized rehabilitation strategies. The findings underscore the potential of AI-driven approaches to revolutionize biomechanical research and clinical practice, offering scalable and personalized solutions for joint mechanics analysis.