A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures

Abstract

This study evaluated the effects of different implant designs and biomaterials on oblique tibial fractures and demonstrated that finite element results can be accurately predicted using machine learning models. The SVM algorithm showed superior performance, with prediction errors of approximately 0.24-0.41% MAE and 0.27-0.49% root mean square error (RMSE) for maximum stress, and 0.03-0.15% MAE and 0.03-0.23% RMSE for total displacement in the training and test sets, respectively. In comparison, MLP and DT exhibited higher errors. These findings highlight the potential of data-driven approaches in biomechanical analyses and their contribution to developing clinical decision support systems.

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