ABSTRACT
Background:
Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in
communities but is limited by abbreviated
protocols and variation in quality. We developed and tested
artificial intelligence (AI) models to automate the
detection of underdiagnosed
cardiomyopathies from cardiac POCUS.
Methods:
In a development set of 290,245 transthoracic echocardiographic videos across the Yale-New Haven
Health System (YNHHS), we used augmentation approaches and a customized loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network (CNN) that discriminates HCM (
hypertrophic cardiomyopathy) and ATTR-CM (
transthyretin amyloid cardiomyopathy) from controls without known
disease. We evaluated the final model across independent, internal and external, retrospective cohorts of individuals
who underwent cardiac POCUS across YNHHS and Mount Sinai
Health System (MSHS)
emergency departments (EDs) (2011-2024) to prioritize key views and validate the diagnostic and prognostic performance of single-view
screening protocols.
Findings:
We identified 33,127
patients (median age 61 [IQR 45-75] years, n=17,276 [52·2%]
female) at YNHHS and 5,624 (57 [IQR 39-71] years, n=1,953 [34·7%]
female) at MSHS with 78,054 and 13,796 eligible cardiac POCUS videos, respectively. An AI-enabled single-view
screening approach successfully discriminated HCM (AUROC of 0·90 [YNHHS] & 0·89 [MSHS]) and ATTR-CM (YNHHS AUROC of 0·92 [YNHHS] & 0·99 [MSHS]). In YNHHS, 40 (58·0%) HCM and 23 (47·9%) ATTR-CM cases had a positive screen at median of 2·1 [IQR 0·9-4·5] and 1·9 [IQR 1·0-3·4] years before
clinical diagnosis. Moreover, among 24,448 participants without known
cardiomyopathy followed over 2·2 [IQR 1·1-5·8] years, AI-POCUS
probabilities in the highest (vs lowest) quintile for HCM and ATTR-CM conferred a 15% (adj.HR 1·15 [95%CI 1·02-1·29]) and 39% (adj.HR 1·39 [95%CI 1·22-1·59]) higher age- and
sex-adjusted
mortality risk, respectively.
Interpretation:
We developed and validated an AI framework that enables scalable, opportunistic
screening of treatable
cardiomyopathies wherever POCUS is used.
Funding:
National
Heart,
Lung and
Blood Institute, Doris Duke Charitable
Foundation, BridgeBio.