The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography


Rebecca A. Jonas, Thomas Jefferson University
Emil Barkovich, The George Washington University School of Medicine and Health Sciences
Andrew D. Choi, The George Washington University School of Medicine and Health Sciences
William F. Griffin, The George Washington University School of Medicine and Health Sciences
Joanna Riess, The George Washington University School of Medicine and Health Sciences
Hugo Marques, NOVA Medical School - Faculdade de Ciências Médicas, Universidade Nova de Lisboa
Hyuk Jae Chang, Yonsei University College of Medicine
Jung Hyun Choi
Joon Hyung Doh, Inje University Paik Hospital
Ae Young Her, Kangwon National University
Bon Kwon Koo, Seoul National University Hospital
Chang Wook Nam, Keimyung University
Hyung Bok Park, Kwandong University, College of Medicine
Sang Hoon Shin, Dongguk University Ilsan Hospital
Jason Cole, Cardiology Associates of Mobile
Alessia Gimelli, Gabriele Monasterio Foundation
Muhammad Akram Khan, Cardiac Center of Texas
Bin Lu, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
Yang Gao, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
Faisal Nabi, Houston Methodist Hospital
Ryo Nakazato, St. Luke's International Hospital Tokyo
U. Joseph Schoepf, Medical University of South Carolina
Roel S. Driessen, Amsterdam UMC - University of Amsterdam
Michiel J. Bom, Amsterdam UMC - University of Amsterdam
Randall C. Thompson, Mid America Heart Institute - Kansas City
James J. Jang, San Jose Medical Center
Michael Ridner, LLC
Chris Rowan, Renown Institute for Heart and Vascular Health
Erick Avelar, Oconee Heart and Vascular Center at St Mary's Hospital
Philippe Généreux, Gagnon Cardiovascular Institute at Morristown Medical Center
Paul Knaapen, Amsterdam UMC - University of Amsterdam
Guus A. de Waard, Amsterdam UMC - University of Amsterdam

Document Type

Journal Article

Publication Date



Clinical Imaging






Artificial intelligence; Atherosclerosis; CCTA; Coronary artery disease; Coronary computed tomography angiography; Image quality


Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370–400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

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