Fatigue Analysis and Validation of a Deep Learning-Enhanced Finite Element Model for Acetabular Cup Screw Fixation in Total Hip Arthroplasty: A Comparative Biomechanical Assessment
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Abstract
Total hip arthroplasty (THA) is a surgical procedure that replaces
damaged hip joints with artificial implants, effectively treating
conditions like osteoarthritis, rheumatoid arthritis, and avascular
necrosis. Despite its success, challenges such as implant loosening,
dislocation, and mechanical failure often necessitate revision surgery.
Advances in preoperative planning, including the "press-fit" approach
and screw fixation, aim to improve outcomes, by maximizing the bony
ingrowth between metal and native bone (Fig. 1). Computational tools
like finite element analysis (FEA) enable detailed biomechanical
simulations to optimize implant stability and reduce complications.
Integrating deep learning (DL) with FEA further enhances predictive
modeling for personalized THA planning. This study develops a DL-FEA
framework to optimize screw fixation, improving the accuracy and
long-term success of THA procedures.
Objective:
(1) to determine the optimal screw configuration for securing the
acetabular cup using FEA
(2) to assess whether a DL algorithm can accurately predict stress-strain
patterns in fixed THA implants
Hypothesis:
• Optimized screw configuration will improve load distribution,
enhance implant stability, and promote bone ingrowth
• Trained DL model will serve as a faster, alternative tool for
stress-strain analysis
Subjects
Affiliations
- California Health Sciences University College of Osteopathic Medicine
- Department of Orthopaedic Surgery University of California-Davis
- Mechanical Engineering Department University of Texas