Ma Xiaokang, Xu Jinhuang, Fu Jie, Liu Qiang
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, 511300, China.
Ann Biomed Eng. 2025 Jul 15. doi: 10.1007/s10439-025-03798-9.
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.
半月板挤出（ME）已被确定为导致膝关节功能障碍和骨关节炎进展的关键因素。传统的有限元分析（FEA）方法虽然准确，但计算成本高且耗时，限制了它们在实时临床评估和大规模研究中的应用。本研究提出了一种几何深度学习（GDL）模型来预测膝关节软组织的生物力学反应，特别关注不同程度半月板挤出的影响。该模型基于有限元分析（FEA）得出的数据进行训练，并利用先进的人工智能算法，在保持高预测准确性的同时显著减少了计算时间。与FEA结果的验证表明，GDL模型能够可靠地预测应力和位移分布，关键性能指标包括平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、峰值位置百分比误差（PEatPEAK）和峰值百分比误差（PEinPEAK）。与传统的FEA工作流程相比，GDL模型省去了耗时的预处理步骤，能够进行实时或近实时的生物力学评估。这一创新为膝关节力学提供了快速的见解，有助于临床决策、手术规划和个性化康复策略。研究结果强调了人工智能驱动方法在革新生物力学研究和临床实践方面的潜力，为关节力学分析提供了可扩展的个性化解决方案。
