Computational modeling methods combined with non-invasive imaging technologies have exhibited great potential and unique opportunities to model new bone formation in scaffold tissue engineering, offering an effective alternate and viable complement to laborious and time-consuming in vivo studies. However, existing numerical approaches are still highly demanding computationally in such multiscale problems. To tackle this challenge, we propose a machine learning (ML)-based approach to predict bone ingrowth outcomes in bulk tissue scaffolds. The proposed in silico procedure is developed by correlating with a dedicated longitudinal (12-month) animal study on scaffold treatment of a major segmental defect in sheep tibia. Comparison of the ML-based time-dependent prediction of bone ingrowth with the conventional multilevel finite element (FE2) model demonstrates satisfactory accuracy and efficiency. The ML-based modeling approach provides an effective means for predicting in vivo bone tissue regeneration in a subject-specific scaffolding system.
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We acknowledge financial support from the Australian Research Council (ARC) through the Discovery (DP180104200, Q.L. and M.V.S.) and ARC Industrial Transformation Training Centre (IC170100022, Q.L. and H.Z.). The Artemis HPC provided by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney, is acknowledged.
The authors declare no competing interests.
Peer review information Nature Computational Science thanks Jose A. Sanz-Herrera, Sara Esteban and Zhiyong Li for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.
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Wu, C., Entezari, A., Zheng, K. et al. A machine learning-based multiscale model to predict bone formation in scaffolds. Nat Comput Sci 1, 532–541 (2021). https://doi.org/10.1038/s43588-021-00115-x