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

Andrew D Choi; Hugo Marques; Vishak Kumar; William F Griffin; Habib Rahban; Ronald P Karlsberg; Robert K Zeman; Richard J Katz; James P Earls

doi:10.1016/j.jcct.2021.05.004

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

J Cardiovasc Comput Tomogr. 2021 Nov-Dec;15(6):470-476. doi: 10.1016/j.jcct.2021.05.004. Epub 2021 Jun 12.

Authors

Andrew D Choi¹, Hugo Marques², Vishak Kumar³, William F Griffin⁴, Habib Rahban⁵, Ronald P Karlsberg⁵, Robert K Zeman⁴, Richard J Katz³, James P Earls⁴

Affiliations

¹ 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.
² 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.
³ Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
⁴ Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA.
⁵ Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA.

PMID: 34127407
DOI: 10.1016/j.jcct.2021.05.004

Abstract

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.

Keywords: Artificial intelligence; Atherosclerosis; Cardiac computed tomography; Coronary artery disease; Heart attack; Machine learning.

Publication types

Multicenter Study

MeSH terms

Artificial Intelligence
Atherosclerosis* / diagnostic imaging
Computed Tomography Angiography
Constriction, Pathologic
Coronary Angiography
Coronary Artery Disease* / diagnostic imaging
Coronary Stenosis* / diagnostic imaging
Humans
Intelligence
Predictive Value of Tests
Tomography, X-Ray Computed