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The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFR<jats:sub>AI<\/jats:sub>) to computational fluid dynamics CT-derived FFR (FFR<jats:sub>CT<\/jats:sub>) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFR<jats:sub>AI<\/jats:sub> model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFR<jats:sub>CT<\/jats:sub> and FFR measurements in this retrospective proof of concept study. FFR<jats:sub>AI<\/jats:sub> was compared with FFR<jats:sub>CT<\/jats:sub> regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR\u2009\u2264\u20090.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFR<jats:sub>AI<\/jats:sub> in predicting FFR\u2009\u2264\u20090.80 were 91% (10\/11), 82% (23\/28), 67% (10\/15), 96% (23\/24), and 85% (33\/39), respectively. Corresponding values for FFR<jats:sub>CT<\/jats:sub> were 82% (9\/11), 75% (21\/28), 56% (9\/16), 91% (21\/23), and 77% (30\/39), respectively. Diagnostic accuracy did not differ significantly between FFR<jats:sub>AI<\/jats:sub> and FFR<jats:sub>CT<\/jats:sub> (<jats:italic>p<\/jats:italic>\u2009=\u20090.12). FFR<jats:sub>AI<\/jats:sub> performed similarly to FFR<jats:sub>CT<\/jats:sub> for predicting intermediate-grade coronary stenoses with FFR\u2009\u2264\u20090.80. These findings suggest FFR<jats:sub>AI<\/jats:sub> as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.<\/jats:p>","DOI":"10.1007\/s10554-024-03173-0","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T12:01:40Z","timestamp":1720094500000},"page":"1875-1880","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis"],"prefix":"10.1007","volume":"40","author":[{"given":"Benjamin","family":"Peters","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Fran\u00e7ois","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rolf","family":"Symons","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouter M.A.","family":"Franssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Nchimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Ghekiere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,4]]},"reference":[{"key":"3173_CR1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1093\/eurheartj\/ehz425","volume":"41","author":"J Knuuti","year":"2020","unstructured":"Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. 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None of the other authors has any conflicts of interest to declare. This research did not receive financial support from any funding agencies in the public, commercial, or not-for-profit sectors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}