计算机科学
有限元法
人工智能
生物信息学
补语(音乐)
生物医学工程
骨愈合
骨形成
机器学习
脚手架
工程类
化学
生物
结构工程
解剖
基因
表型
内分泌学
数据库
互补
生物化学
作者
Chi Wu,Ali Entezari,Keke Zheng,Jianguang Fang,Hala Zreiqat,Grant P. Steven,Michael V. Swain,Qing Li
标识
DOI:10.1038/s43588-021-00115-x
摘要
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. The study develops a machine learning approach for predicting bone regeneration in an additively manufactured bioceramic scaffold, which is correlated with an in vivo sheep model, exhibiting effectiveness for solving such a multiscale modeling problem.
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