Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method.
Artificial intelligence
Computed tomography
Coronary artery calcium
Deep learning
Journal
European heart journal open
ISSN: 2752-4191
Titre abrégé: Eur Heart J Open
Pays: England
ID NLM: 9918282081406676
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
22
06
2023
revised:
14
09
2023
accepted:
26
10
2023
medline:
30
11
2023
pubmed:
30
11
2023
entrez:
30
11
2023
Statut:
epublish
Résumé
To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images. Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.
Identifiants
pubmed: 38035036
doi: 10.1093/ehjopen/oead113
pii: oead113
pmc: PMC10683040
doi:
Types de publication
Journal Article
Langues
eng
Pagination
oead113Informations de copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.
Déclaration de conflit d'intérêts
Conflict of interest: Dr. Fujimoto has a research agreement with Fujifilm Corporation, Tokyo, Japan, that is related to this study.
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