Computational modeling of bicuspid aortopathy: Towards personalized risk strategies.
Bicuspid aortic valve
Computational-fluid dynamic
Finite-element analysis
Journal
Journal of molecular and cellular cardiology
ISSN: 1095-8584
Titre abrégé: J Mol Cell Cardiol
Pays: England
ID NLM: 0262322
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
26
02
2019
revised:
09
04
2019
accepted:
26
04
2019
pubmed:
3
5
2019
medline:
23
6
2020
entrez:
4
5
2019
Statut:
ppublish
Résumé
This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.
Identifiants
pubmed: 31047985
pii: S0022-2828(18)31034-4
doi: 10.1016/j.yjmcc.2019.04.026
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
122-131Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.