Assessment of shape-based features ability to predict the ascending aortic aneurysm growth.
aorta
ascending aorta aneurysm
biomechanical features
cardiovascular diseases
classification
growth rate
machine learning
risk assessment
Journal
Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006
Informations de publication
Date de publication:
2023
2023
Historique:
received:
16
12
2022
accepted:
24
02
2023
entrez:
23
3
2023
pubmed:
24
3
2023
medline:
24
3
2023
Statut:
epublish
Résumé
The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter
Identifiants
pubmed: 36950300
doi: 10.3389/fphys.2023.1125931
pii: 1125931
pmc: PMC10025384
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1125931Informations de copyright
Copyright © 2023 Geronzi, Haigron, Martinez, Yan, Rochette, Bel-Brunon, Porterie, Lin, Marin-Castrillon, Lalande, Bouchot, Daniel, Escrig, Tomasi, Valentini and Biancolini.
Déclaration de conflit d'intérêts
Authors LG, AM, KY, and MR were employed by Ansys France. The remaining 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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