Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 신상훈 | * |
dc.date.accessioned | 2023-02-22T16:30:29Z | - |
dc.date.available | 2023-02-22T16:30:29Z | - |
dc.date.issued | 2023 | * |
dc.identifier.issn | 1936-878X | * |
dc.identifier.other | OAK-33010 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/264004 | - |
dc.description.abstract | Background: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. Objectives: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence–enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab–interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). Methods: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration–cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. Results: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. Conclusions: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab–interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275) © 2023 The Authors | * |
dc.language | English | * |
dc.publisher | Elsevier Inc. | * |
dc.subject | artificial intelligence | * |
dc.subject | atherosclerosis | * |
dc.subject | coronary artery disease | * |
dc.subject | coronary computed tomography | * |
dc.subject | coronary CTA | * |
dc.subject | fractional flow reserve | * |
dc.subject | quantitative coronary angiography | * |
dc.title | AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy | * |
dc.type | Article | * |
dc.relation.issue | 2 | * |
dc.relation.volume | 16 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 193 | * |
dc.relation.lastpage | 205 | * |
dc.relation.journaltitle | JACC: Cardiovascular Imaging | * |
dc.identifier.doi | 10.1016/j.jcmg.2021.10.020 | * |
dc.identifier.scopusid | 2-s2.0-85147128903 | * |
dc.author.google | Griffin W.F. | * |
dc.author.google | Choi A.D. | * |
dc.author.google | Riess J.S. | * |
dc.author.google | Marques H. | * |
dc.author.google | Chang H.-J. | * |
dc.author.google | Choi J.H. | * |
dc.author.google | Doh J.-H. | * |
dc.author.google | Her A.-Y. | * |
dc.author.google | Koo B.-K. | * |
dc.author.google | Nam C.-W. | * |
dc.author.google | Park H.-B. | * |
dc.author.google | Shin S.-H. | * |
dc.author.google | Cole J. | * |
dc.author.google | Gimelli A. | * |
dc.author.google | Khan M.A. | * |
dc.author.google | Lu B. | * |
dc.author.google | Gao Y. | * |
dc.author.google | Nabi F. | * |
dc.author.google | Nakazato R. | * |
dc.author.google | Schoepf U.J. | * |
dc.author.google | Driessen R.S. | * |
dc.author.google | Bom M.J. | * |
dc.author.google | Thompson R. | * |
dc.author.google | Jang J.J. | * |
dc.author.google | Ridner M. | * |
dc.author.google | Rowan C. | * |
dc.author.google | Avelar E. | * |
dc.author.google | Généreux P. | * |
dc.author.google | Knaapen P. | * |
dc.author.google | de Waard G.A. | * |
dc.author.google | Pontone G. | * |
dc.author.google | Andreini D. | * |
dc.author.google | Earls J.P. | * |
dc.contributor.scopusid | 신상훈(7403646689;27868133100) | * |
dc.date.modifydate | 20240426130307 | * |