Neural network prediction of load from the morphology of trabecular bone

TL;DR

This paper presents a neural network-based method to predict bone loading conditions from trabecular bone density, addressing inverse problem challenges like non-uniqueness and measurement inaccuracies.

Contribution

The study introduces a neural network approach for inverse prediction of bone loads from density distributions, overcoming key challenges in the field.

Findings

Successfully predicts tissue adaptation loads from density data.

Addresses non-uniqueness and measurement inaccuracies in load prediction.

Demonstrates robustness of the neural network method.

Abstract

Bone adaptation models are often solved in the forward direction, meaning that the response of bone to a given set of loads is determined by running a bone tissue adaptation model. The model is generally solved using a numerical technique such as the finite element model. Conversely, one may be interested in the loads that have resulted in a given state of bone. This is the inverse of the former problem. Even though the inverse problem has several applications, it has not received as much attention as the forward problem, partly because solving the inverse problem is more difficult. A nonlinear system identification technique is needed for solving the inverse problem. In this study, we use artificial neural networks for prediction of tissue adaptation loads from a given density distribution of trabecular bone. It is shown that the proposed method can successfully identify the loading parameters from the density distribution of the tissue. Two important challenges for all load prediction algorithms are the non-uniqueness of the solution of the inverse problem and the inaccuracies in the measurement of the morphology of the tissue. Both challenges are studied, and it is shown that the load prediction technique proposed in this paper can overcome both.