An artificial intelligence (AI) based evaluation of coronary computed tomography angiography (CTA) in close agreement to blinded, core lab-interpreted quantitative coronary angiography, enables an accurate, rapid identification and exclusion of high-grade stenosis, according to findings published in the Journal of the American College of Cardiology: Cardiovascular Imaging.

Clinical reads of coronary CTA sometimes result in overestimation of the severity of coronary disease stenosis. AI-based solutions may exist. Researchers sought to compare the performance of AI-enabled quantitative coronary computed tomography angiography (AI-QCT) for detection and grading of coronary stenosis with core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR).

To accomplish this, researchers conducted a retrospective study of 303 patients (64±10 years of age, 71% men) from the multinational Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE; identifier: NCT02173275) trial with coronary CTA, FFR, and QCA data analyzed with a 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.

Continue Reading

Stenosis of 50% or greater was found in 0, 1, 2, and 3 coronary vessel territories, with a prevalence of 32%, 35%, 21%, and 13%, respectively. Average AI-QCT analysis time was 10.3±2.7 minutes. For patients with stenosis of 50% or greater, AI demonstrated per-patient sensitivity of 94%, specificity of 68%, positive predictive value of 81%, negative predictive value of 90%, and accuracy of 84%. For detection of stenosis of 70% or greater, per-patient sensitivity was 94%, specificity was 82%, positive predictive value was 69%, negative predictive value was 97%, and accuracy was 86%.

Correlation was high between stenoses detected by AI-QCT vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P <.001 for both). False positive AI-QCT findings were noted in 7.3% of vessels.

This study was limited by using post hoc analysis instead of a prospective clinical trial.

“In this analysis of the multinational CREDENCE trial, an AI-based evaluation demonstrated high diagnostic performance for the identification, exclusion, discrimination, and correlation to a QCA reference standard,” the study authors wrote. “Given the rapid turnaround time of this AI-QCT and its superior performance to previous coronary CTA core lab and site readers, this approach may augment clinical coronary CTA interpretation.”

Disclosure: Some study authors declared affiliations with biotech, pharmaceutical, and/or device companies. Please see the original reference for a full list of authors’ disclosures.


Griffin WF, Choi AD, Riess JS, et al. AI evaluation of stenosis on coronary CT angiography, comparison with quantitative coronary angiography and fractional flow reserve: A CREDENCE trial substudy. JACC Cardiovasc Imaging. Published online February 16, 2022. doi:10.1016/j.jcmg.2021.10.020