Artificial neural network supported design of a lattice-based artificial spinal disc for restoring patient-specific anisotropic behaviors

Abstract

To tackle the challenge in artificial spinal disc (ASD) design of restoring the mechanics of a natural disc, this study proposes an innovative lattice-based ASD for reproducing a patient-specific anisotropic rotational response, inspired by the design freedom provided by lattice structures. Motivated by the great potential of machine learning to improve computational design processes, a method is proposed for computationally efficient topology optimization using artificial neural networks (ANNs) and a subsequent member sizing for automating the design of patient-specific ASDs. The results reported in this study show a good match between the optimized ASDs’ six rotational stiffnesses with those of both L2-L3 and L4-L5 human lumbar discs. Additionally, the fast convergence rate of the optimization verifies the application of ANNs and the proposed strategy to reduce the design space by formulating the design problem as optimizing the unit-cell distribution in a predefined grid. Therefore, the study demonstrates that a lattice-based ASD is able to reproduce patient-specific anisotropic rotational response and that a machine-learning-based method improves the computational efficiency of an automated design process to produce personalized ASD designs.