## 3 Results
### Design Optimization Outcomes  
The design optimization process was conducted to refine key implant parameters before training the deep learning surrogate model. The Goodness of Fit, Response, Local Sensitivity, and Spider Graphs generated by the Response Surface simulation allowed for the identification of the most influential variables affecting stress distribution and fixation stability. This optimization ensured that the dataset used for deep learning training was composed of well-validated simulations, enhancing the model's ability to predict stress-strain responses with greater precision and accuracy. These simulation processes are summarized in Figure 11:  
* Goodness of Fit: This figure illustrates the model's predictive accuracy by comparing simulated values against experimental (reference) results. A high Goodness of Fit score indicates strong correlation and model reliability, ensuring that stress-strain distributions are accurately represented. Notably, the results indicate that higher accuracy is achieved when screw positioning is optimized, confirming the importance of precise placement.
* 3D Response Graph: This graph visualizes how different input parameters, such as screw position and material properties, influence key output variables, like stress and strain. Non-linear trends are highlighted, allowing for the identification of optimal configurations that minimize stress concentrations and maximize implant stability. The results show that improper screw angularation leads to uneven stress distribution, which can compromise implant integrity.
* Local Sensitivity Graph: This figure quantifies how small variations in design parameters affect the overall implant performance. By analyzing sensitivity across multiple variables, the graph helps determine which parameters have the most significant impact on stress distribution and fixation stability. The results emphasize that even minor deviations in bone density significantly impact screw fixation strength, reinforcing the need for patient-specific planning.
* Local Sensitivity Curves: These curves offer a detailed visualization of the sensitivity analysis results, illustrating how minor changes in specific input parameters influence the biomechanical behavior of the implant. This aids in refining implant design to enhance performance and reduce failure risks. The findings suggest that higher-grade titanium screws reduce peak stress concentrations, enhancing long-term stability.
* Spider Chart (Radar Chart): The Radar Chart provides a comprehensive assessment of trade-offs between multiple design variables, such as implant orientation, bone quality, and screw fixation techniques. By mapping these interactions, the figure aids in selecting the most balanced and effective screw configuration for optimal implant performance. The results confirm that increasing screw length improves load distribution but may also elevate stress in adjacent bone structures, requiring a careful balance in design choices.  
These figures collectively contribute to the iterative optimization process, ensuring that the final implant design is robust, efficient, and tailored for enhanced surgical outcomes.  
Figure 11: Simulations generated for design optimization. (**A**) Goodness of Fit, (**B**) 3D Response Graph, (**C**) Local Sensitivity Graph, (**D**) Local Sensitivity Curves, and (**E**) Spider Chart.