Optimizing Screw Fixation in Total Hip Arthroplasty: A Deep Learning and Finite Element Analysis Approach - Jacob Stroud; Emily T. Yan; Jacob Anthony; Kyle Walker; Chung-Hyun Goh (Applied Sciences)
Total hip arthroplasty (THA) is a widely performed procedure to restore hip function in patients with degenerative joint diseases. Traditional “press-fit” fixation methods rely on sufficient bone quality for stability, but additional screw fixation is often necessary for patients with suboptimal bone conditions. However, comprehensive studies utilizing predictive modeling to optimize screw placement strategies in THA remain limited. This study integrates finite element analysis (FEA) with deep learning (DL) to optimize screw fixation strategies, enhancing implant stability and reducing revision rates. The design optimization process was conducted to refine key implant parameters before training the deep learning surrogate model. By utilizing advanced simulation techniques—including Goodness of Fit analysis, Response Graphs, Local Sensitivity Analysis, and Spider Charts—critical factors influencing stress distribution and fixation stability were identified. The optimization process ensured that the dataset used for deep learning training consisted of well-validated simulations, thereby improving the predictive accuracy of stress–strain responses. The findings indicate that optimized screw placement significantly improves load distribution, reducing stress concentrations and enhancing long-term implant stability. The comparative analysis of FEA and DL results showed that the DL-FEA surrogate model successfully replicated deformation patterns, though with a mean squared error (MSE) of 24.06%. While this suggests room for improvement, the model demonstrates potential for streamlining surgical planning. A comparative assessment with traditional methods highlights the advantages of DL-FEA in reducing computational time while maintaining precision. Future improvements will focus on refining the DL model, increasing the dataset size, and incorporating clinical validation. These findings contribute to developing a computational protocol for personalized acetabular cup fixation, with implications for reducing revision rates and improving surgical outcomes.