{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:27:42Z","timestamp":1780572462786,"version":"3.54.1"},"reference-count":21,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2023,5]]},"abstract":"Abstract<\/jats:title>Aims<\/jats:title>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\u2010quantitative computed tomography (AI\u2010QCT) for patients being referred for nonemergent invasive coronary angiography (ICA).<\/jats:p><\/jats:sec>Methods<\/jats:title>CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American\u00a0College\u00a0of\u00a0Cardiology\u00a0(ACC)\/American\u00a0Heart\u00a0Association\u00a0(AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud\u2010based software (Cleerly, Inc.) that performs AI\u2010QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI\u2010QCT guided findings were related to MACE at 1\u2010year follow\u2010up.<\/jats:p><\/jats:sec>Results<\/jats:title>Seven hundred forty\u2010seven\u00a0stable patients (60\u2009\u00b1\u200912.2 years, 49% women) were included. Using AI\u2010QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI\u2010QCT to identify obstructive coronary stenosis at the \u226550% and \u226570% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI\u2010QCT\u2010identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis\u2009<\u200950%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI\u2010QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>In stable patients referred for ACC\/AHA guideline\u2010indicated nonemergent ICA, application of artificial intelligence and machine learning for AI\u2010QCT can significantly reduce ICA rates and costs with no change in 1\u2010year MACE.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.23995","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T10:14:34Z","timestamp":1677492874000},"page":"477-483","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Atherosclerosis Imaging Quantitative Computed Tomography (AI\u2010QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial"],"prefix":"10.1002","volume":"46","author":[{"given":"Yumin","family":"Kim","sequence":"first","affiliation":[{"name":"The George Washington University School of Medicine Washington District of Columbia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1500-0428","authenticated-orcid":false,"given":"Andrew D.","family":"Choi","sequence":"additional","affiliation":[{"name":"The George Washington University School of Medicine Washington District of Columbia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anha","family":"Telluri","sequence":"additional","affiliation":[{"name":"The George Washington University School of Medicine Washington District of Columbia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabella","family":"Lipkin","sequence":"additional","affiliation":[{"name":"The George Washington University School of Medicine Washington District of Columbia 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Gangwon\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ae\u2010Young","family":"Her","sequence":"additional","affiliation":[{"name":"Kangwon National University Hospital Gangwon\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cezary","family":"Kepka","sequence":"additional","affiliation":[{"name":"National Institute of Cardiology Warsaw Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang\u2010Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Wonju Severance Hospital Gangwon\u2010do South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Won","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea University Guro Hospital Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang\u2010Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Chung\u2010Ang University Hospital Seoul South 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