ArticleFrontiers in bioengineering and biotechnology2026
A Gyroid TPMS-based machine learning framework for rapid prediction of cancellous bone mechanical properties.
Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: High-fidelity finite-element analysis of cancellous bone is computationally expensive, particularly when large parameter spaces must be explored. 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. Methods: Trabecular tissue Young's modulus ( Results: The finite-element model reproduced published uniaxial compression responses. BPNN achieved the best prediction of Discussion: The proposed framework provides an accurate and computationally efficient approach for rapid prediction, parameter screening, response-surface analysis, and structural design exploration within the investigated Gyroid model space.
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