## 3 Results
### Comparative Analysis Results  
The comparative analysis focused on four key aspects: accuracy, computational efficiency, empirical validation, and robustness.  
1. Accuracy findings: The DL-FEA model demonstrated reasonable accuracy in replicating traditional FEA results, with an MSE of 24.06%. Minor deviations in high-stress regions indicate areas for future refinement.
2. Computational efficiency gains: traditional FEA required extensive computation time for complex loading cases, whereas DL-FEA provided near-instantaneous predictions, highlighting its potential for real-time surgical planning.
3. Empirical validation outcomes: the stress distribution patterns from DL-FEA closely aligned with empirical studies on acetabular cup fixation, supporting its reliability.
4. Robustness and sensitivity insights: sensitivity analyses confirmed that DL-FEA maintained stability across varying input conditions, successfully adapting to changes in bone density and implant orientation, demonstrating its applicability for personalized implant optimization.  
To evaluate the effectiveness of DL-FEA compared to traditional FEA, multiple assessment methodologies were employed. The key findings demonstrate that DL-FEA offers substantial improvements over traditional FEA in several aspects:  
1. Computational cost reduction: Traditional FEA requires extensive computation for complex loading scenarios, making real-time surgical planning impractical. In contrast, once trained, the DL-FEA model generates near-instantaneous predictions, eliminating the need for repeated full-scale simulations and significantly reducing processing time.
2. Streamlined optimization process: Traditional FEA relies on manual parameter tuning and iterative analysis, making design optimization labor-intensive. DL-FEA automates this process using machine learning algorithms to predict optimal stress distributions, making data-driven optimization faster, and more efficient.
3. Comparable predictive accuracy: While traditional FEA remains the gold standard for biomechanical modeling, the DL-FEA model demonstrated a reasonable MSE of 24.06%, effectively replicating deformation and stress-strain patterns. Future improvements, including expanding the dataset and refining neural network architectures, could enhance the accuracy further.  
The results demonstrate that while traditional FEA remains essential for biomechanical modeling, DL-FEA presents a viable alternative with significant computational advantages. Future refinements will focus on improving the deep learning model, increasing dataset diversity, and incorporating additional boundary conditions to enhance accuracy and clinical applicability.  
Additionally, the results indicate that the number, length, and angulation of screws significantly influence acetabular cup stability. Increasing the number of screws generally reduces micromotion, but excessive screws can lead to higher stress concentrations in adjacent bone structures. Optimal screw length is crucial for anchoring in denser bone regions while avoiding stress shielding, but we only considered the standard screw length for this study. Screw angulation directly affects load distribution, with improper angles increasing the stress around screw heads. A well-balanced configuration minimizes implant displacement while maintaining favorable stress distribution.