Multi-objective computational design optimization of a Total Disc Replacement implant

TL;DR

This paper presents a finite element simulation-driven multi-objective optimization approach for designing Total Disc Replacement implants to improve biomechanics and reduce revision risks.

Contribution

It introduces a novel automated optimization process combining neural networks and hybrid algorithms for TDR implant design, focusing on biomechanics and stability.

Findings

Optimized designs improved multi-objective scores by 14.6% and 36.1%.

Implant migration risk was reduced by 24.8%.

The approach demonstrates potential for addressing complex clinical challenges.

Abstract

While cervical arthroplasty using Total Disc Replacement (TDR) implants is an established treatment for persistent neck and arm pain, revision rates limit it from reaching its full potential. To address the underlying complications, we developed finite element simulation-driven design optimizations for a TDR's bone-implant interface and motion-preservation features. These automated processes explored high-dimensional design spaces iteratively through analysis of design variations interplay with spinal structures. The optimizations were metamodel-based using artificial neural networks and a hybrid optimizer. They optimized the motion-preservation zone towards replicating the asymptomatic spinal segment's ligaments strain profiles and its facet joint force profiles during main motions. This design process aims to minimize the risk for postoperative pain, avoidable degeneration and to restore segmental biomechanics, to prevent adjacent segment effects. Designs with single articulation and with dual articulation (with a mobile insert) were optimized. The bone-implant interface was optimized with the aim to minimize the risk for subsidence and implant migration. The optimizations improved the multi-objective value of the bone-implant interface by 14.6% and that of the motion-preservation zone by 36.1%. Implant migration, the leading cause of revisions, was reduced by 24.8%. With this, we show the potential of simulation-driven implant design optimization for addressing complex clinical challenges.