CT ​Evaluation ​by ​Artificial ​Intelligence ​for ​Atherosclerosis, Stenosis and Vascular ​Morphology ​(CLARIFY): ​A ​Multi-center, international study.

Autor: Choi AD; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA; Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA. Electronic address: adchoi@mfa.gwu.edu., Marques H; Centro Hospitalar Universitário de Lisboa Central - Serviço de Radiologia do Hospital de Santa Marta, Lisboa, Portugal; Nova Medical School, Faculdade de Ciências Médicas, Lisboa, Portugal., Kumar V; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA., Griffin WF; Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA., Rahban H; Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA., Karlsberg RP; Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA., Zeman RK; Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA., Katz RJ; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA., Earls JP; Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
Jazyk: angličtina
Zdroj: Journal of cardiovascular computed tomography [J Cardiovasc Comput Tomogr] 2021 Nov-Dec; Vol. 15 (6), pp. 470-476. Date of Electronic Publication: 2021 Jun 12.
DOI: 10.1016/j.jcct.2021.05.004
Abstrakt: Background: Atherosclerosis evaluation by coronary computed tomography angiography (CCTA) is promising for coronary artery disease (CAD) risk stratification, but time consuming and requires high expertise. Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations. We hypothesized AI aided analysis allows for rapid, accurate evaluation of vessel morphology and stenosis.
Methods: This was a multi-site study of 232 patients undergoing CCTA. Studies were analyzed by FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by three L3 readers. CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category.
Results: AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively. Bland-Altman plots depict agreement between expert reader and AI determined maximal diameter stenosis for per-vessel (mean difference -0.8%; 95% CI 13.8% to -15.3%) and per-patient (mean difference -2.3%; 95% CI 15.8% to -20.4%). L3 and AI agreed within one CAD-RADS category in 228/232 (98.3%) exams per-patient and 923/924 (99.9%) vessels on a per-vessel basis. There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution.
Conclusions: AI-aided approach to CCTA interpretation determines coronary stenosis and CAD-RADS category in close agreement with consensus of L3 expert readers. There was a wide range of atherosclerosis identified through AI.
Competing Interests: Declaration of competing interest Equity, Cleerly, Inc (ADC, HM, JPE). All other authors report no disclosures.
(Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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