Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial

Yumin Kim, Andrew D. Choi*, Anha Telluri, Isabella Lipkin, Andrew J. Bradley, Alfateh Sidahmed, Rebecca Jonas, Daniele Andreini, Ravi Bathina, Andrea Baggiano, Rodrigo Cerci, Eui-Young Choi, Jung-Hyun Choi, So-Yeon Choi, Namsik Chung, Jason Cole, Joon-Hyung Doh, Sang-Jin Ha, Ae-Young Her, Cezary KepkaJang-Young Kim, Jin Won Kim, Sang-Wook Kim, Woong Kim, Gianluca Pontone, Todd C. Villines, Iksung Cho, Ibrahim Danad, Ran Heo, Sang-Eun Lee, Ji Hyun Lee, Hyung-Bok Park, Ji-min Sung, Tami Crabtree, James P. Earls, James K. Min, Hyuk-Jae Chang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. Conclusions: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Original languageEnglish
Pages (from-to)477-483
Number of pages7
JournalClinical cardiology
Issue number5
Early online date2023
Publication statusPublished - May 2023

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