A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.
brain biomechanics
cerebral contusion
controlled cortical impact
finite element modeling
machine learning
tissue-level injury criteria
traumatic brain injury
Journal
Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513
Informations de publication
Date de publication:
2021
2021
Historique:
received:
24
05
2021
accepted:
10
08
2021
entrez:
25
10
2021
pubmed:
26
10
2021
medline:
26
10
2021
Statut:
epublish
Résumé
Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification
Identifiants
pubmed: 34692652
doi: 10.3389/fbioe.2021.714128
pii: 714128
pmc: PMC8531645
doi:
Types de publication
Journal Article
Langues
eng
Pagination
714128Informations de copyright
Copyright © 2021 Menichetti, Bartsoen, Depreitere, Vander Sloten and Famaey.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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