{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T20:19:22Z","timestamp":1791404362866,"version":"4.3.3"},"reference-count":39,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning\u2013based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the \u226575th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47\u20134.78];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the \u226575th percentile of total plaque volume.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Per-patient age- and sex-specific distributions for deep learning\u2013based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the \u226575th percentile.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circimaging.124.016958","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:00:31Z","timestamp":1727672431000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":27,"title":["Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis"],"prefix":"10.1161","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4676-2433","authenticated-orcid":true,"given":"Robert J.H.","family":"Miller","sequence":"first","affiliation":[{"name":"Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.)."},{"name":"Department of Cardiac Sciences, University of Calgary, Alberta, Canada (R.J.J.M.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9040-6896","authenticated-orcid":true,"given":"Nipun","family":"Manral","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Lin","sequence":"additional","affiliation":[{"name":"Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.)."},{"name":"Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia (A.L., S.J.N., D.T.W.)."},{"name":"Monash Heart, Monash Health, Melbourne, Victoria, Australia (A.L., S.J.N., 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