## 1 Introduction
Biomechanical stress testing, traditionally performed with cadavers or synthetic femurs, has been effectively replicated with computational in silico models [23]. Ultrasound imaging and computed tomography scanning can be useful in some cases, but, while accessible, these methods are limited in how much they can be manipulated to simulate different scenarios [24]. FEA is a versatile yet underutilized tool in surgical planning. It creates a virtual representation of the test object as smaller, interconnected elements. Material properties and boundary conditions are assigned to each element to simulate real-world behaviors. In surgical planning, FEA allows researchers and surgeons to analyze the biomechanical integrity of the hip joint under various conditions, providing insights into stress distribution, load-bearing capacities, and potential areas of concern. For screw placement, FEA enables a comprehensive examination of the effect of variables, such as screw size, number, length, and orientation, on implant stability and longevity. This approach optimizes screw placement strategies to minimize stress concentrations, assess micromotion, and reduce the risk of complications, like periprosthetic fractures. As a result, the likelihood of revision surgery and associated complications decreases. Moreover, FEA can tailor screw placement to patient-specific factors, such as bone density and morphology, leading to a customized approach to THA. Integrating FEA into preoperative planning enhances the understanding of biomechanical complexities and improves surgical techniques, increasing the success and durability of THA procedures. Finite element analysis (FEA) has long been valuable for biomechanical modeling, particularly in total hip arthroplasty (THA). By creating a meshed representation of the implant-bone interface, FEA enables the precise simulation of stress-strain distributions  
Figure 2: Additional screw fixation for acetabular cup. Reproduced with permission from AO Surgery Reference (Copyright by AO Foundation, Switzerland) [8]. under various loading conditions. However, despite its advantages, traditional FEA faces challenges, including high computational costs, as simulations require significant processing power and time. Additionally, FEA-based design optimization is cumbersome, involving manual parameter adjustments and iterative simulations without guaranteeing a globally optimized solution. To address these challenges, deep learning (DL)-assisted FEA (DL-FEA) has emerged as a promising alternative. DL-FEA leverages pre-trained neural networks to predict stress-strain distributions, significantly reducing computation time while maintaining accuracy. This study explores the integration of DL with FEA to optimize screw fixation strategies, enhancing automation, efficiency, and predictive capabilities. Deep learning (DL), a subset of machine learning, trains neural networks with large datasets to recognize patterns and make predictions. Although machine learning has only recently been introduced in healthcare, it is already improving surgical training, preoperative planning, and risk assessment. In clinical orthopedics, it is primarily applied for diagnostics, such as classifying injury patterns or detecting occult issues [25]. This study aims to achieve the following: (1) identify the optimal screw configuration for securing the acetabular cup using FEA and (2) evaluate whether a DL algorithm can accurately predict these stress-strain patterns in screw-fixed THA implants. We hypothesize that an optimized screw placement will enhance load distribution, improve implant stability, and promote bone ingrowth, while the trained DL model will serve as a faster, surrogate tool for stress-strain analysis. An optimized screw placement is expected to distribute loads more effectively and optimize the amount of stress and strain needed for bony ingrowth over time between the bony pelvis and acetabular cup.