Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience.
Acute coronary syndrome (ACS)
computed tomography angiography
fractional flow reserve
machine learning
myocardial
Journal
Cardiovascular diagnosis and therapy
ISSN: 2223-3652
Titre abrégé: Cardiovasc Diagn Ther
Pays: China
ID NLM: 101601613
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
entrez:
24
9
2020
pubmed:
25
9
2020
medline:
25
9
2020
Statut:
ppublish
Résumé
Computed tomography (CT)-derived fractional flow reserve (FFR For this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFR FFR Our preliminary data show that FFR
Sections du résumé
BACKGROUND
BACKGROUND
Computed tomography (CT)-derived fractional flow reserve (FFR
METHODS
METHODS
For this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFR
RESULTS
RESULTS
FFR
CONCLUSIONS
CONCLUSIONS
Our preliminary data show that FFR
Identifiants
pubmed: 32968637
doi: 10.21037/cdt-20-381
pii: cdt-10-04-820
pmc: PMC7487397
doi:
Types de publication
Journal Article
Langues
eng
Pagination
820-830Informations de copyright
2020 Cardiovascular Diagnosis and Therapy. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/cdt-20-381). The authors have no conflicts of interest to declare.
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