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

William F. Griffin, Department of Radiology and Division of Cardiology, George Washington University School of Medicine, Washington, DC, USA.
Andrew D. Choi, Department of Radiology and Division of Cardiology, George Washington University School of Medicine, Washington, DC, USA. Electronic address: https://twitter.com/AChoiHeart.
Joanna S. Riess, Department of Radiology and Division of Cardiology, George Washington University School of Medicine, Washington, DC, USA.
Hugo Marques, Department of Cardiology, Faculdade de Ciências Médicas, Nova Medical School, Lisbon, Portugal.
Hyuk-Jae Chang, Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.
Jung Hyun Choi, Department of Cardiology, Pusan National University Hospital, Busan, South Korea.
Joon-Hyung Doh, Division of Cardiology, Inje University Ilsan Paik Hospital, South Korea.
Ae-Young Her, Department of Cardiology, Kang Won National University Hospital, Chuncheon, South Korea.
Bon-Kwon Koo, Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.
Chang-Wook Nam, Cardiovascular Center, Keimyung University Dongsan Hospital, Daegu, South Korea.
Hyung-Bok Park, Division of Cardiology, Department of Internal Medicine, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea.
Sang-Hoon Shin, Division of Cardiology, Department of Internal Medicine, Ewha Women's University Seoul Hospital, Seoul, South Korea.
Jason Cole, Department of Cardiology, Mobile Cardiology Associates, Mobile, Alabama, USA.
Alessia Gimelli, Department of Imaging, Fondazione Toscana Gabriele Monasterio, Pisa, Italy.
Muhammad Akram Khan, Department of Cardiology, Cardiac Center of Texas, McKinney, Texas, USA.
Bin Lu, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Beijing, China.
Yang Gao, Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan.
Faisal Nabi, Department of Cardiology, Houston Methodist Hospital, Houston, Texas, USA.
Ryo Nakazato, Cardiovascular Center, St. Luke's International Hospital, Tokyo, Japan.
U Joseph Schoepf, Department of Radiology, Medical University of South Carolina, Charleston, South Carolina, USA.
Roel S. Driessen, Department of Cardiology, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, the Netherlands.
Michiel J. Bom, Department of Cardiology, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, the Netherlands.
Randall Thompson, Department of Cardiology, St. Luke's Mid America Heart Institute, Kansas City, Missouri, USA.
James J. Jang, Department of Cardiology, Kaiser Permanente Hospital, Oakland, California, USA.
Michael Ridner, Heart Center Research, LLC, Huntsville, Alabama, USA.
Chris Rowan, Department of Cardiology, Renown Heart and Vascular Institute, Reno, Nevada, USA.
Erick Avelar, Oconee Heart and Vascular Center, St Mary's Hospital, Athens, Georgia, USA.
Philippe Généreux, Gagnon Cardiovascular Institute, Morristown Medical Center, Morristown, New Jersey, USA.
Paul Knaapen, Department of Cardiology, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, the Netherlands.
Guus A. de Waard, Department of Cardiology, Amsterdam University Medical Center, VU University Medical Center, Amsterdam, the Netherlands.
Gianluca Pontone, Department of Cardiology, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Daniele Andreini, Department of Cardiology, Centro Cardiologico Monzino IRCCS, Milan, Italy.

Document Type

Journal Article

Publication Date

2-15-2022

Journal

JACC. Cardiovascular imaging

DOI

10.1016/j.jcmg.2021.10.020

Keywords

CCTA; artificial intelligence; atherosclerosis; coronary artery disease; coronary computed tomography; fractional flow reserve; quantitative coronary angiography

Abstract

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).

Department

Radiology

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