{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:19:07Z","timestamp":1792790347029,"version":"4.5.0"},"posted":{"date-parts":[[2026,7,24]]},"group-title":"Cardiovascular Medicine","reference-count":37,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"American Heart Association","award":["26CDA1612298"],"award-info":[{"award-number":["26CDA1612298"]}],"id":[{"id":"https:\/\/ror.org\/013kjyp64","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Institute on Aging","award":["P30AG021342"],"award-info":[{"award-number":["P30AG021342"]}],"id":[{"id":"https:\/\/ror.org\/049v75w11","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Institute on Aging","award":["R01AG089981"],"award-info":[{"award-number":["R01AG089981"]}],"id":[{"id":"https:\/\/ror.org\/049v75w11","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Heart Lung and Blood Institute","award":["R01HL167858"],"award-info":[{"award-number":["R01HL167858"]}],"id":[{"id":"https:\/\/ror.org\/012pb6c26","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Heart Lung and Blood Institute","award":["K23HL153775"],"award-info":[{"award-number":["K23HL153775"]}],"id":[{"id":"https:\/\/ror.org\/012pb6c26","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Heart Lung and Blood Institute","award":["K23HL181318"],"award-info":[{"award-number":["K23HL181318"]}],"id":[{"id":"https:\/\/ror.org\/012pb6c26","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Yale Center for Clinical Investigation\/Yale CTSA\/NCATS","award":["UL1 TR001863"],"award-info":[{"award-number":["UL1 TR001863"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,10,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Excess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has traditionally required advanced imaging.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>We investigated whether AI-enabled echocardiography could characterize epicardial adiposity and identify residual cardiometabolic risk beyond body mass index (BMI).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    We developed\n                    <jats:italic>PanAdipo<\/jats:italic>\n                    , a video-based deep learning model in 1,114,441 videos from 28,797 studies across the Yale-New Haven Health System (YNHHS; 2016-2022), using report-derived expert reader labels of prominent EAT (2.3% of studies).\n                    <jats:italic>PanAdipo<\/jats:italic>\n                    was evaluated in five cohorts: a\n                    <jats:italic>temporally<\/jats:italic>\n                    distinct YNHHS transthoracic echocardiography (TTE) set (n=4,588 patients contributing 5,130 studies), an emergency department point-of-care ultrasound (POCUS) cohort (n=10,957), the geographically distinct MIMIC-IV dataset (n=4,549), the community-based Multi-Ethnic Study of Atherosclerosis (MESA Exam 6, n=2,740), and in held-out YNHHS patients with paired coronary CT angiography within 12 months (n=5,167). Analyses examined discrimination of the report label and of CT-defined high indexed EAT volume, correlation with paired cardiac CT phenotypes, and BMI-adjusted associations with cardiometabolic biomarkers and newly documented metabolic disease.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    In the held-out health system test set, PanAdipo identified prominent EAT documented in clinical reports with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.82-0.91) across all views and of up to 0.93 when combining apical 4-chamber and parasternal views. Against paired CT-derived measurements, PanAdipo discriminated age- and sex-specific high EAT (EAT\/heart volume \u226595th percentile) with AUROCs of 0.91 to 0.95 across age-sex groups and correlated more strongly with indexed EAT volume (\u03c1=0.76) than with BMI (\u03c1=0.41, all P&lt;0.001); PanAdipo alone explained 53% of indexed EAT volume variance, versus 23% for age, sex, and BMI combined. In MESA, higher scores were associated with an adverse metabolic profile (higher HOMA-IR [\u03b2=0.17 per SD; 95% CI, 0.11-0.22]) after adjustment for age, sex, and BMI. In health system TTE and POCUS cohorts, higher scores were associated with newly documented MASLD\/MASH (BMI-adjusted HR\n                    <jats:sub>pooled<\/jats:sub>\n                    1.31 [1.03-1.67] per SD of log[PanAdipo]) and hyperlipidemia (1.11 [1.04-1.18]).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>AI-enabled echocardiography provides a scalable digital biomarker that characterizes prominent epicardial adiposity and is associated with an adverse cardiometabolic profile, highlighting a role for echocardiography in metabolic risk stratification.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>CONDENSED ABSTRACT<\/jats:title>\n                  <jats:p>Epicardial adipose tissue (EAT) is linked to cardiometabolic risk, but its assessment has traditionally required advanced imaging. We developed PanAdipo, a deep learning model that derives a digital biomarker of EAT from routine echocardiography. PanAdipo tracks CT-defined epicardial adiposity (\u03c1=0.76; AUROC 0.91-0.95 for age-\/sex-specific high indexed-EAT), yet correlates modestly with body mass index (BMI; \u03c1=0.19-0.41). After BMI adjustment, higher scores were associated with insulin resistance in a community-based cohort and newly documented metabolic disease across health systems. AI-enabled echocardiography may provide a scalable window into epicardial adiposity, with potential applications for enriching trial populations and monitoring response to cardiometabolic therapies.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.07.22.26358690","type":"posted-content","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:50:18Z","timestamp":1784937018000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-Enabled Echocardiography Identifies Prominent Epicardial Adipose Tissue Associated With an Adverse Cardiometabolic Profile"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3197-2657","authenticated-orcid":false,"given":"Arya","family":"Aminorroaya","sequence":"first","affiliation":[{"name":"Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, 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