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AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy
- Title
- AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy
- Authors
- Griffin W.F.; Choi A.D.; Riess J.S.; Marques H.; Chang H.-J.; Choi J.H.; Doh J.-H.; Her A.-Y.; Koo B.-K.; Nam C.-W.; Park H.-B.; Shin S.-H.; Cole J.; Gimelli A.; Khan M.A.; Lu B.; Gao Y.; Nabi F.; Nakazato R.; Schoepf U.J.; Driessen R.S.; Bom M.J.; Thompson R.; Jang J.J.; Ridner M.; Rowan C.; Avelar E.; Généreux P.; Knaapen P.; de Waard G.A.; Pontone G.; Andreini D.; Earls J.P.
- Ewha Authors
- 신상훈
- SCOPUS Author ID
- 신상훈
- Issue Date
- 2023
- Journal Title
- JACC: Cardiovascular Imaging
- ISSN
- 1936-878X
- Citation
- JACC: Cardiovascular Imaging vol. 16, no. 2, pp. 193 - 205
- Keywords
- artificial intelligence; atherosclerosis; coronary artery disease; coronary computed tomography; coronary CTA; fractional flow reserve; quantitative coronary angiography
- Publisher
- Elsevier Inc.
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- 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
- DOI
- 10.1016/j.jcmg.2021.10.020
- Appears in Collections:
- 의료원 > 의료원 > Journal papers
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