Computational evaluation of different numerical tools for the prediction of proximal femur loads from bone morphology
We ideally estimate patient-specific loads from bone geometry and density measurement. We combine FE, bone remodeling models, and machine learning techniques. Linear regression and ANN computed a good load prediction with error less than 2%. The support vector machine technique predicts higher relative errors. ANN with multiple outputs obtained a high accuracy in the prediction of a known bone density.
linear regression support vector machine bone density artificial neuronal network loading conditions inverse bone remodeling problem