A machine learning-based multiscale model to predict bone formation in scaffolds.
Journal
Nature computational science
ISSN: 2662-8457
Titre abrégé: Nat Comput Sci
Pays: United States
ID NLM: 101775476
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
01
02
2021
accepted:
19
07
2021
medline:
1
8
2021
pubmed:
1
8
2021
entrez:
13
1
2024
Statut:
ppublish
Résumé
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 (FE
Identifiants
pubmed: 38217252
doi: 10.1038/s43588-021-00115-x
pii: 10.1038/s43588-021-00115-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
532-541Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.
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