A high-throughput framework for predicting three-dimensional structural–mechanical relationships of human cranial bones using a deep learning-based method

Cranial bone injuries significantly impact human health, potentially leading to death or permanent disability, and mechanical responses are crucial predictors of cranial damage. Predicting mechanical responses through medical imaging is an efficient method that streamlines the process by eliminating the need for intermediate steps, such as diagnostic testing and biomedical analysis. Although previous studies have successfully predicted 1D sequences or 2D sectional mechanical attributes from medical imaging, these approaches are limited in their ability to capture the anisotropic characteristics of cranial bone. As a complex osseous material, cranial bone has significant directional dependencies in its microstructural features, which directly influence its macroscopic mechanical responses under loading conditions. In this study, we aim to introduce a deep learning-based high-throughput framework to correlate the three-dimensional mechanical responses and three-dimensional cranial microstructures derived from medical images. First, micro-CT scans of 40 human cranial samples, spanning an average age of 82.5 years, were performed to capture microstructural information. Next, 2000 representative volume element (RVE) units were randomly and automatically extracted from these scans to characterize the cranial microstructures. Following this, 2000 stress and 2000 strain fields were derived from finite element simulations based on these RVE units, and subsequently validated through quasi-static compression experiments. An optimized U-Net-based deep learning network was employed to link the macro-property stress–strain response with the three-dimensional cranial microstructures. The proposed framework demonstrates robust performance in predicting the spatial mechanical behavior based on microstructural inputs, showing high and consistent similarity between the predictions and ground truth. Overall, the high-throughput nature of this framework facilitates the handling of large-scale data, enabling comprehensive and efficient analysis that is crucial for predicting mechanical responses. By elucidating structure–property relationships, this approach can enhance the accuracy of injury diagnosis and aid in the development of tailored treatment plans, effectively bridging the gap between structural morphology and mechanical functionality.