AI Evaluation of Stenosis on Coronary CT Angiography, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

作者：W. Griffin, A. Choi, J. Riess, H. Marques, H. Chang, J. Choi, J. Doh, A. Her, B. Koo, C. Nam, Hyung-Bok Park, Sangshoon Shin, Jason H. Cole, A. Gimelli, M. Khan, B. Lu, Yang Gao, F. Nabi, R. Nakazato, U. Schoepf, R. Driessen, M. Bom, Randall C. Thompson, James J. Jang, Michael L. Ridner, C. Rowan, E. Avelar, P. Généreux, P. Knaapen, G. D. de Waard, G. Pontone, D. Andreini, J. Earls · 发表于：JACC Cardiovascular Imaging · 年份：2022 · DOI：10.1016/j.jcmg.2021.10.020 · 被引用次数：169 · 研究领域：Medicine

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%, respecti...