Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint

Abstract

The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.