Towards an Automated Computational Workflow to Assess Primary Stability in Total Hip Arthroplasty

TL;DR

This paper introduces an automated system to predict hip implant stability using 3D planning and simulations, aiming to improve surgical outcomes and reduce revision surgeries.

Contribution

The novel contribution is an automated computational workflow combining preoperative planning and finite element modeling for predicting implant stability.

Findings

The automated pipeline significantly reduces simulation setup time on high-performance computing systems.

The framework evaluates implant stability through bone-implant contact interaction analysis.

The method is demonstrated using open-source data and a commercial implant, showing its potential for personalized implant strategies.

Abstract

Total hip arthroplasty is one of the most common and rapidly growing surgical procedures, with over one million cases performed annually in the United States. Despite high success rates, revision surgeries remain a significant concern due to complications such as aseptic loosening, often resulting from inadequate primary implant stability. This study presents an automated computational framework that integrates three-dimensional preoperative planning and finite element modeling to predict the primary stability of hip implants. Data obtained from the virtual surgery phase are used to generate subject-specific finite element models, which are executed on high-performance computing systems. The simulation evaluates implant stability by analyzing the contact interaction between the bone and the implant. The pipeline is demonstrated using data from the open-source HFValid collection and a commercial implant. Automation substantially reduced the time required to set up simulations, improving the efficiency on high-performance infrastructure. This integrated computational approach bridges the gap between biomechanical modeling and clinical decision-making and can serve as a preclinical tool for identifying personalized implant strategies and for conducting large-scale virtual cohort studies.

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