CHSU Discovery

Total hip arthroplasty (THA) commonly utilizes press-fit fixation, but screws are often required for suboptimal bone quality. This study integrates finite element analysis (FEA) and deep learning (DL) to optimize screw placement, improving implant stability, load distribution, and fatigue resistance while reducing computational time. FEA simulations evaluated stress, strain, deformation, and fatigue failure risk, while fatigue analysis identified high-risk regions under cyclic loading, emphasizing optimized screw positioning. A design optimization process refined implant parameters, and a non-linear regression algorithm trained a DL surrogate model for stress prediction. The fatigue analysis revealed stress concentrations at the screw-bone interface, highlighting potential failure zones. The DL-FEA model successfully replicated deformation and fatigue life predictions, achieving mean squared error (MSE) of 0.0011, with R² = 0.93 and Pearson coefficient = 0.97, confirming strong agreement test data from traditional FEA. Additionally, The DL-FEA model reduced computational time from several hours to approximately 30 minutes per scenario, enabling faster and more robust preoperative planning and efficient implant evaluation. Future work will focus on expanding datasets, improving fatigue life estimation, and validating with clinical and experimental data. Incorporating adaptive AI-driven predictive modeling can further refine the accuracy of implant performance simulations, ultimately enhancing personalized orthopedic treatment strategies. Incorporating dynamic motion analysis and patient-specific modeling will further enhance prediction accuracy. This study provides a computational framework for personalized implant design, potentially reducing revision rates and improving long-term outcomes for THA patients.