ArticlemedRxiv : the preprint server for health sciences2026
AI-Enabled Echocardiography Identifies an Adverse Epicardial Adiposity Phenotype Associated with Cardiometabolic Dysfunction.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Excess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has traditionally required advanced imaging. We tested whether AI-enhanced echocardiography could enable scalable phenotyping of adverse epicardial adiposity and identify individuals at increased cardiometabolic risk. Methods: We developed Results: In the held-out health system test set, PanAdipo discriminated prominent EAT with an AUROC of 0.91 (95% CI, 0.88-0.94), exceeding conventional measures of cardiac function and structure. In explainability analyses, the model's attention localized to the epicardial area across views and throughout the cardiac cycle. On paired cardiac CT imaging, the PanAdipo score correlated most strongly with epicardial adiposity (Spearman ρ=0.75; P<10 Conclusions: AI-enabled echocardiography provides a scalable, view-agnostic biomarker that characterizes adverse epicardial adiposity and is associated with cardiometabolic dysfunction, highlighting a new role for echocardiography in CKM risk stratification.
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