## 4 Discussion  
FEA simulations demonstrated that acetabular cup deformation is significantly influenced by loading conditions and screw configuration. The highest deformation occurred inthe no-screw configuration, indicating reduced implant stability. The addition of screws generally improved fixation, with the 2-screw configuration providing greater stability at the cost of slightly higher stress concentrations in adjacent bone structures. These findings suggest that screw fixation plays a crucial role in optimizing load distribution and reducing the risk of implant loosening. However, the trade-off between additional fixation and potential bone stress must be carefully considered to avoid complications, such as stress shielding or periprosthetic fractures. Quantitative analysis showed that the 2-screw configuration reduced implant micromotion by approximately 18% compared to the no-screw model, while also lowering peak von Mises stress by 12%. However, stress concentrations near the screw heads increased by nearly 22%, highlighting a potential risk for bone weakening in those regions. Similarly, increasing the screw length improved the anchoring strength but led to stress shielding effects in adjacent cortical bone, suggesting an optimal balance is necessary to minimize bone resorption over time. These findings reinforce the need for the computational optimization of screw placement to ensure biomechanical stability without introducing excessive localized stresses. The integration of DL with FEA enabled the predictive modeling of stress-strain distributions, reducing computational time compared to traditional FEA simulations. While the DL-FEA model successfully captured deformation patterns, its mean squared error (MSE) of 24.06% highlights areas for refinement. The primary sources of discrepancy include dataset limitations and the need for more sophisticated non-linear regression techniques to enhance accuracy. Despite these limitations, the DL-FEA framework presents a promising approach for streamlining implant design and preoperative planning. Further improvements in data preprocessing and feature engineering could help refine the predictive capabilities of the model. From a clinical perspective, this study underscores the importance of patient-specific screw placement. While increasing the number of screws generally enhances stability, excessive fixation can lead to localized stress concentrations, potentially weakening the surrounding bone. Proper screw angular and placement in high-density bone regions are critical factors in ensuring optimal fixation. The findings support the development of personalized screw placement strategies that account for individual bone quality and anatomical variations, ultimately improving long-term implant success rates. This reinforces the need for advanced imaging techniques and computational models to guide intraoperative decision-making and optimize patient-specific surgical strategies. Future refinements to the DL model should focus on expanding the dataset to incorporate a wider range of bone densities, screw configurations, and loading conditions. Additionally, integrating clinical imaging data from CT or MRI scans will enhance the model's applicability for patient-specific surgical planning. Further validation through experimental cadaveric testing and retrospective clinical data analysis will be critical to confirming the real-world accuracy of the DL-FEA predictions. These validation steps will provide direct empirical comparisons between model-predicted stress-strain distributions and actual biomechanical outcomes, helping bridge the gap between computational modeling and clinical practice. By leveraging computational modeling and machine learning, this study provides a foundation for optimizing acetabular cup fixation in THA. The proposed approach holds potential for improving surgical outcomes by enabling data-driven, patient-specific decision-making in orthopedic implant design and placement.