Griffin, William FChoi, Andrew DRiess, Joanna SMarques, HugoPinto Marques, HugoChang, Hyuk-JaeChoi, Jung HyunDoh, Joon-HyungHer, Ae-YoungKoo, Bon-KwonNam, Chang-WookPark, Hyung-BokShin, Sang-HoonCole, JasonGimelli, AlessiaKhan, Muhammad AkramLu, BinGao, YangNabi, FaisalNakazato, RyoSchoepf, U JosephDriessen, Roel SBom, Michiel JThompson, RandallJang, James JRidner, MichaelRowan, ChrisAvelar, ErickGénéreux, PhilippeKnaapen, Paulde Waard, Guus APontone, GianlucaAndreini, DanieleEarls, James P2022-04-042022-04-042023-021936-878XPURE: 42309356PURE UUID: 4c628e50-2958-4ba7-a5d6-935eaf359d1ePubMed: 35183478ORCID: /0000-0003-3540-0488/work/110981261Scopus: 85147128903WOS: 000954714300007http://hdl.handle.net/10362/135838Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. 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).2657709engartificial intelligenceatherosclerosiscoronary artery diseasecoronary computed tomographycoronary CTAfractional flow reservequantitative coronary angiographyjournal article10.1016/j.jcmg.2021.10.020