{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:12:17Z","timestamp":1766139137804,"version":"3.41.2"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":198,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005633","name":"Finnish Foundation for Cardiovascular Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005633","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Finnish State Research Funding"},{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016175","name":"Chulabhorn Royal Academy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016175","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cleerly, Inc"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>AI-QCTischaemia is an FDA-cleared novel artificial intelligence\u2013guided method that utilizes features from coronary computed tomography angiography (CCTA) to predict myocardial ischaemia.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To identify factors associated with discrepancy between AI-QCTischaemia and positron emission tomography (PET) perfusion.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>Six hundred and sixty-two patients with suspected obstructive coronary artery disease (CAD) on CCTA and undergoing [15O]H2O PET were analysed using AI-QCTischaemia. Multivariable logistic regression identified factors associated with discrepancy. Perfusion homogeneity was measured by relative flow reserve. A total of 209 (32%) patients showed discrepancies: 62 (9%) exhibited normal AI-QCTischaemia but abnormal perfusion (false negative AI-QCTischaemia), whereas 147 (22%) had abnormal AI-QCTischaemia despite normal perfusion (false positive AI-QCTischaemia). False positive AI-QCTischaemia patients (vs. true positive) were more often females, older, with less typical angina, and less advanced CAD. In multivariable analysis, typical angina [OR 95% CI: 1.796 (1.015\u20133.179), P = 0.044], diameter stenosis per 1% increase [1.058 (1.036\u20131.080), P &amp;lt; 0.001], and percent atheroma volume per 1% increase [1.103 (1.051\u20131.158), P &amp;lt; 0.001] significantly predicted true positive, while age was inversely associated [0.955 (0.923\u20130.989), P = 0.010]. False-negative AI-QCTischaemia patients (vs. true negative) were more often males, smokers, with less good CCTA image quality, and more advanced CAD. However, none was significant in multivariable analysis. Furthermore, false-negative AI-QCTischaemia showed more homogenously reduced perfusion by relative flow reserve compared to true positive (median \u00b1 IQR: 0.68 \u00b1 0.15 vs. 0.56 \u00b1 0.23, P &amp;lt; 0.001) and 21 (34%) of false negative showed globally reduced perfusion.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>For abnormal AI-QCTischaemia, younger age, typical angina, more severe stenosis, and more extensive atherosclerosis predicted abnormal PET perfusion. With false negative AI-QCTischaemia, perfusion abnormalities were partly explained by microvascular disease.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjimp\/qyaf033","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T05:07:16Z","timestamp":1744866436000},"source":"Crossref","is-referenced-by-count":1,"title":["Factors affecting the performance of a novel artificial intelligence-based coronary computed tomography-derived ischaemia algorithm"],"prefix":"10.1093","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7076-3829","authenticated-orcid":false,"given":"Peerapon","family":"Kiatkittikul","sequence":"first","affiliation":[{"name":"Turku PET Centre, Turku University Hospital and University of Turku , P. O. Box 52, Turku FI-20521 ,","place":["Finland"]},{"name":"National Cyclotron and PET Centre, Chulabhorn Hospital, Chulabhorn Royal Academy , Bangkok ,","place":["Thailand"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5819-4635","authenticated-orcid":false,"given":"Teemu","family":"Maaniitty","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University Hospital and University of Turku , P. O. Box 52, Turku FI-20521 ,","place":["Finland"]},{"name":"Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital , Turku FI-20520 ,","place":["Finland"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-6596","authenticated-orcid":false,"given":"Sarah","family":"B\u00e4r","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University Hospital and University of Turku , P. O. Box 52, Turku FI-20521 ,","place":["Finland"]},{"name":"Department of Cardiology, Bern University Hospital Inselspital , Bern ,","place":["Switzerland"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4671-7667","authenticated-orcid":false,"given":"Takeru","family":"Nabeta","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Leiden University Medical Center , Leiden ,","place":["The Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7368-0500","authenticated-orcid":false,"given":"Jeroen J","family":"Bax","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Leiden University Medical Center , Leiden ,","place":["The Netherlands"]},{"name":"Heart Center, Turku University Hospital and University of Turku , Turku ,","place":["Finland"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2488-0893","authenticated-orcid":false,"given":"Antti","family":"Saraste","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University Hospital and University of Turku , P. O. Box 52, Turku FI-20521 ,","place":["Finland"]},{"name":"Heart Center, Turku University Hospital and University of Turku , Turku ,","place":["Finland"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3156-9593","authenticated-orcid":false,"given":"Juhani","family":"Knuuti","sequence":"additional","affiliation":[{"name":"Turku PET Centre, Turku University Hospital and University of Turku , P. O. Box 52, Turku FI-20521 ,","place":["Finland"]},{"name":"Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital , Turku FI-20520 ,","place":["Finland"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"2025041701070519500_qyaf033-B1","doi-asserted-by":"crossref","first-page":"3415","DOI":"10.1093\/eurheartj\/ehae177","article-title":"2024 ESC guidelines for the management of chronic coronary syndromes","volume":"45","author":"Vrints","year":"2024","journal-title":"Eur Heart J"},{"key":"2025041701070519500_qyaf033-B2","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1093\/eurheartj\/ehy267","article-title":"The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability","volume":"39","author":"Knuuti","year":"2018","journal-title":"Eur Heart J"},{"key":"2025041701070519500_qyaf033-B3","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/j.amjcard.2015.07.078","article-title":"Meta-analysis of diagnostic performance of coronary computed tomography angiography, computed tomography perfusion, and computed tomography-fractional flow reserve in functional myocardial ischemia assessment versus invasive fractional flow reserve","volume":"116","author":"Gonzalez","year":"2015","journal-title":"Am J Cardiol"},{"key":"2025041701070519500_qyaf033-B4","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s00259-021-05341-z","article-title":"Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT\/CT, PET\/CT, and cardiac CT","volume":"48","author":"Slart","year":"2021","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2025041701070519500_qyaf033-B5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.2214\/AJR.21.27289","article-title":"Coronary CTA with AI-QCT interpretation: comparison with myocardial perfusion imaging for detection of obstructive stenosis using invasive angiography as reference standard","volume":"219","author":"Lipkin","year":"2022","journal-title":"AJR Am J Roentgenol"},{"key":"2025041701070519500_qyaf033-B6","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.jcct.2022.03.001","article-title":"Coronary CTA plaque volume severity stages according to invasive coronary angiography and FFR","volume":"16","author":"Min","year":"2022","journal-title":"J Cardiovasc Comput Tomogr"},{"author":"Cleerly Launches Cleerly ISCHEMIA Solution for Heart Disease Analysis [Internet]. 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