Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study
Authors: Oikonomou Evangelos K. , Vaid Akhil , Holste Gregory , Coppi Andreas , McNamara Robert L. , Baloescu Cristiana , Krumholz Harlan M. , Wang Zhangyang , Apakama Donald J. , Nadkarni Girish N. , Khera Rohan 
Published: March 2024

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

Research in Context

Evidence before this study

Point-of-care ultrasonography (POCUS) can support clinical decision-making at the point-of-care as a direct extension of the physical exam. POCUS has benefited from the increasing availability of portable and smartphone-adapted probes and even artificial intelligence (AI) solutions that can assist novices in acquiring basic views. However, the diagnostic and prognostic inference from POCUS acquisitions is often limited by the short acquisition duration, suboptimal scanning conditions, and limited experience in identifying subtle pathology that goes beyond the acute indication for the study. Recent solutions have shown the potential of AI-augmented phenotyping in identifying traditionally under-diagnosed cardiomyopathies on standard transthoracic echocardiograms performed by expert operators with strict protocols. However, these are not optimized for opportunistic screening using videos derived from typically lower-quality POCUS studies. Given the widespread use of POCUS across communities, ambulatory clinics, emergency departments (ED), and inpatient settings, there is an opportunity to leverage this technology for diagnostic and prognostic inference, especially for traditionally under-recognized cardiomyopathies, such as hypertrophic cardiomyopathy (HCM) or transthyretin amyloid cardiomyopathy (ATTR-CM) which may benefit from timely referral for specialized care.

Added value of this study

We present a multi-label, view-agnostic, video-based convolutional neural network adapted for POCUS use, which can reliably discriminate cases of ATTR-CM and HCM versus controls across more than 90,000 unique POCUS videos acquired over a decade across EDs affiliated with two large and diverse health systems. The model benefits from customized training that emphasizes low-quality acquisitions as well as off-axis, non-traditional views, outperforming view-specific algorithms and approaching the performance of standard TTE algorithms using single POCUS videos as the sole input. We further provide evidence that among reported controls, higher probabilities for HCM or ATTR-CM-like phenotypes are associated with worse long-term survival, suggesting possible under-diagnosis with prognostic implications. Finally, among confirmed cases with previously available POCUS imaging, positive AI-POCUS screens were seen at median of 2 years before eventual confirmatory testing, highlighting an untapped potential for timely diagnosis through opportunistic screening.

Implications of all available evidence

We define an AI framework with excellent performance in the automated detection of underdiagnosed yet treatable cardiomyopathies. This framework may enable scalable screening, detecting these disorders years before their clinical recognition, thus improving the diagnostic and prognostic inference of POCUS imaging in clinical practice.