Data Sheet 1_A Gyroid TPMS-based machine learning framework for rapid prediction of cancellous bone mechanical properties.zip

This study developed a rapid surrogate-modeling framework integrating parametric Gyroid modeling, finite-element analysis, and machine learning to predict the apparent mechanical properties of cancellous-bone models.</p>Methods<p>Trabecular tissue Young’s modulus (E_t), tissue failure strain (ε_t), bone volume fraction (BV/TV), and degree of anisotropy (DA) were used as inputs.

A database of 200 parameter combinations was generated using Latin hypercube sampling, with apparent Young’s modulus (E_c) and failure stress (σ_c) obtained from finite-element simulations as separate outputs. Multiple linear regression, quadratic polynomial regression, support vector regression, Kriging, and a backpropagation neural network were evaluated using nested five-fold cross-validation.</p>Results<p>The finite-element model reproduced published uniaxial compression responses.