## 5 Conclusions  
This study presents a computational approach that integrates finite element analysis (FEA) and deep learning (DL) to optimize screw fixation strategies in total hip arthroplasty (THA). The results demonstrate that optimized screw placement enhances load distribution, minimizes stress concentrations, and improves implant stability. By leveraging DL-FEA, this approach significantly reduces the computational time compared to traditional FEA while maintaining predictive accuracy.  
Key findings indicate that increasing the number of screws generally improves fixation stability but may also introduce localized stress concentrations in adjacent bone structures. This study underscores the importance of optimizing screw placement, length, and angulation to achieve a balance between implant stability and stress minimization. Furthermore, the results suggest that DL-FEA can serve as a viable alternative to traditional FEA in preoperative planning, offering a more efficient predictive tool for assessing fixation strategies.  
Despite the promising results, some limitations remain. The current model primarily considers static and limited motion conditions, requiring further validation under dynamic loading scenarios, such as gait cycles and impact forces. Additionally, empirical validation through cadaveric studies and clinical imaging is essential to improve the generalizability and applicability of DL-FEA in surgical practice.  
Future research will focus on expanding the dataset to include a broader range of anatomical and biomechanical conditions, incorporating patient-specific modeling from CT/MRI data, and refining deep learning architectures to enhance prediction accuracy. Additionally, comparative analysis with other machine learning methods will be conducted to assess model performance relative to alternative computational approaches.  
By addressing these challenges, DL-FEA can evolve into an indispensable tool for optimizing surgical planning, improving patient-specific implant fixation strategies, and ultimately reducing revision rates in THA. This research provides a pathway toward more precise, data-driven decision-making in orthopedic surgery, contributing to the advancement of personalized medicine in joint replacement procedures.  
Conceptualization, J.S., E.T.Y., K.W. and C.-H.G.; data curation, J.S.; formal analysis, J.S. and E.T.Y.; investigation, J.S. and E.T.Y.; methodology, J.S., E.T.Y., J.A. and C.-H.G.; project administration, C.-H.G.; resources, C.-H.G.; software, E.T.Y. and J.A.; supervision, C.-H.G.; validation, K.W. and C.-H.G.; writing--original draft preparation, E.T.Y.; writing--review and editing, E.T.Y., C.-H.G. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors acknowledge the support of the University of Texas at Tyler, California Health Sciences University College of Osteopathic Medicine, University of California, Davis, and the Runatek Corporation, Dallas.  
The authors declare no conflicts of interest.