1. AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve
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William F. Griffin, Andrew D. Choi, Joanna S. Riess, Hugo Marques, Hyuk-Jae Chang, Jung Hyun Choi, Joon-Hyung Doh, Ae-Young Her, Bon-Kwon Koo, Chang-Wook Nam, Hyung-Bok Park, Sang-Hoon Shin, Jason Cole, Alessia Gimelli, Muhammad Akram Khan, Bin Lu, Yang Gao, Faisal Nabi, Ryo Nakazato, U. Joseph Schoepf, Roel S. Driessen, Michiel J. Bom, Randall Thompson, James J. Jang, Michael Ridner, Chris Rowan, Erick Avelar, Philippe Généreux, Paul Knaapen, Guus A. de Waard, Gianluca Pontone, Daniele Andreini, James P. Earls, and NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
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Radiology, Nuclear Medicine and imaging ,Cardiology and Cardiovascular Medicine - Abstract
Copyright © 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
- Published
- 2023
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