AI-Enabled Echocardiography Identifies an Adverse Epicardial Adiposity Phenotype Associated with Cardiometabolic Dysfunction.

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 PanAdipo, 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 expert reader annotations of prominent EAT (2.5% of studies). PanAdipo was evaluated in four cohorts: a temporally distinct YNHHS TTE set (n=4,588), an emergency-department point-of-care ultrasound cohort across YNHHS (n=10,957), the geographically distinct MIMIC-IV cohort (n=4,549), and the community-based Multi-Ethnic Study of Atherosclerosis (MESA Exam 6, n=2,740). Analyses examined (i) discrimination of prominent EAT; (ii) independence from conventional echocardiographic outputs; (iii) spatial explainability using gradient-weighted class activation mapping; (iv) correspondence with paired CT-derived body composition phenotypes (n=5,594); and (v) age-, sex-, and BMI-independent associations with cardiometabolic biomarkers and incident metabolic disease.

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<10-300) with weaker correlations with other adipose and non-adipose compartments and only modest correlations with BMI across cohorts (ρ=0.19-0.40). In MESA, greater PanAdipo scores were independently associated with higher HOMA-IR and triglycerides, associations that persisted among normoglycemic participants. Higher PanAdipo scores were also associated with newly documented metabolic disease, including MASLD/MASH, after adjustment for BMI (HRpooled 1.25 [1.08-1.44] per 1-SD increment in log[PanAdipo]).

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.