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

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 SchoepfRoel 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

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations.

OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR).

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).

Original languageEnglish (US)
Pages (from-to)193-205
Number of pages13
JournalJACC: Cardiovascular Imaging
Issue number2
StatePublished - Feb 2023


  • artificial intelligence
  • atherosclerosis
  • coronary CTA
  • coronary artery disease
  • coronary computed tomography
  • fractional flow reserve
  • quantitative coronary angiography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine


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