{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T04:52:11Z","timestamp":1791867131507,"version":"4.3.4"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"17","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Angiography\u2010based fractional flow reserve (FFR) techniques offer wire\u2010free alternatives but often require manual segmentation and 3\u2010dimensional reconstruction. Although an artificial intelligence\u2010driven approach automates these steps, validation remains limited. This study investigated the diagnostic performance of an artificial intelligence\u2010driven angiography\u2010based FFR (medipixel FFR [MPFFR]), compared with quantitative flow ratio (QFR) in predicting functionally significant coronary artery stenosis defined by FFR \u22640.80.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">A total of 599 vessels from 452 patients who underwent clinically indicated FFR measurement were prospectively enrolled from 5 university hospitals in Korea. MPFFR used automated processes in frame selection, artificial intelligence contour detection, corresponding points matching, 3\u2010dimensional reconstruction, and analytical hemodynamic modeling. The primary end point was diagnostic accuracy for detecting FFR \u22640.80. Secondary end points were target vessel failure (composite of cardiac death, target\u2010vessel myocardial infarction, and target\u2010vessel revascularization) at 2\u2009years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Mean analysis time of MPFFR was 12.5\u00b11.7 seconds and manual correction was needed in 32 vessels (5.3%). MPFFR showed similar diagnostic performance with QFR (correlation with FFR; MPFFR versus QFR: R=0.885 versus R=0.860,\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      for comparison=0.011; area under the curve to predict FFR \u22640.80; 0.949 versus 0.953,\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      for comparison=0.631). At a median follow\u2010up of 2\u2009years (interquartile range, 1.6\u20132.6\u2009years), patients with MPFFR \u22640.80 had higher risk of target vessel failure than those with MPFFR &gt;0.80 (4.5% versus 0.8%; adjusted hazard ratio, 5.94 [95% CI, 1.27\u201327.91];\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =0.024). C\u2010index to predict target vessel failure was comparable between MPFFR and QFR (0.770 versus 0.753,\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      for comparison=0.469).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">In this multicenter registry, an artificial intelligence\u2010driven angiography\u2010based FFR demonstrated comparable diagnostic accuracy with QFR in identifying functionally significant stenosis, and similar prognostic ability with QFR in terms of target vessel failure at 2\u2009years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Registration<\/jats:title>\n                    <jats:p xml:lang=\"en\">Multicenter QFR Registry; Unique Identifier: NCT03791788.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.049331","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:50:26Z","timestamp":1787226626000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence\u2010Driven Angiographic Quantification of Fractional Flow Reserve: Proof\u2010of\u2010Concept Validation Study"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2337-7826","authenticated-orcid":false,"given":"Seung Hun","family":"Lee","sequence":"first","affiliation":[{"name":"Chonnam National University Medical School","place":["Gwangju, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1428-094X","authenticated-orcid":false,"given":"Dong Hyun","family":"Gim","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine","place":["Seoul, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doyeon","family":"Hwang","sequence":"additional","affiliation":[{"name":"Seoul National University Hospital","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4554-041X","authenticated-orcid":false,"given":"Hyun Kuk","family":"Kim","sequence":"additional","affiliation":[{"name":"University of Chosun College of Medicine","place":["Gwangju, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2506-4216","authenticated-orcid":false,"given":"Hyun","family":"Cho","sequence":"additional","affiliation":[{"name":"Inje University Ilsan Paik Hospital","place":["Goyang, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7242-5217","authenticated-orcid":false,"given":"Sung Eun","family":"Kim","sequence":"additional","affiliation":[{"name":"Inje University Ilsan Paik Hospital","place":["Goyang, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3983-817X","authenticated-orcid":false,"given":"Sung Woo","family":"Cho","sequence":"additional","affiliation":[{"name":"Inje University Ilsan Paik Hospital","place":["Goyang, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7511-9581","authenticated-orcid":false,"given":"Ki Hong","family":"Choi","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine","place":["Seoul, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1440-3583","authenticated-orcid":false,"given":"Taek Kyu","family":"Park","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine","place":["Seoul, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8138-1367","authenticated-orcid":false,"given":"Jeong Hoon","family":"Yang","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine","place":["Seoul, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3986-9152","authenticated-orcid":false,"given":"Young Bin","family":"Song","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine","place":["Seoul, Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7966-9564","authenticated-orcid":false,"given":"Joon\u2010Hyung","family":"Doh","sequence":"additional","affiliation":[{"name":"Inje University Ilsan Paik Hospital","place":["Goyang, Republic of 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