AI-enabled electrocardiography improved structural heart disease discrimination (AUC 0.73 vs 0.52) and reduced time to identify 50% of cases from 81 to 19 days versus standard referral.
Does AI-enabled electrocardiography improve the identification of structural heart disease and accelerate echocardiography triage compared to standard ECG interpretation in a low-resource setting?
AI-enabled ECG interpretation significantly outperforms standard ECG criteria for detecting structural heart disease, allowing for highly efficient triage and accelerated diagnosis in resource-constrained settings.
Absolute Event Rate: 0% vs 0%
Timely diagnosis of structural heart disease (SHD) is often constrained by limited access to echocardiography in low-resource settings. Although electrocardiography (ECG) is widely available, traditional interpretation poorly reflects underlying SHD risk, leading to inefficient referral for echocardiography. We used data from PROVAR+, a large community-based cardiovascular screening program in operation since 2014 in Brazil. In this program, adults underwent 12-lead ECGs paired with either screening point-of-care ultrasound (POCUS) (screening cohort) or comprehensive transthoracic echocardiography (TTE) (imaging cohort). We evaluated the performance of a validated artificial intelligence model in identifying probable SHD from ECG images compared with Minnesota Code–defined major ECG abnormalities, with POCUS as a screening reference. We then assessed the health-system impact of AI-ECG–guided echocardiography referral using paired ECG–TTE data by simulating standard versus AI-prioritized referral strategies under an established health-system echocardiography capacity. Among 3282 individuals in the screening cohort, AI-ECG demonstrated substantially higher discrimination for probable SHD than Minnesota Code–based major ECG abnormalities (area under the curve 0.73 vs 0.52), with higher positive predictive value and significant net reclassification improvement (35%), driven primarily by improved identification of individuals without disease. Decision-curve analysis showed consistently greater net benefit for AI-ECG across clinically relevant referral thresholds. In the imaging cohort ( n = 1475), AI-ECG–guided prioritization accelerated SHD diagnosis under a previously known system capacity of 200 echocardiograms per month. The time to identify 50% of SHD cases was shorter with AI-guided care, with 19 days using AI-ECG-based triage vs 81 days under standard referral, which was consistent across demographic and clinical subgroups. AI-enabled ECG interpretation improved the identification of SHD beyond conventional ECG assessment. Applying AI-ECG to echocardiography referral workflows can substantially accelerate diagnosis under limited imaging capacity, supporting more efficient and equitable use of cardiovascular diagnostic resources.
Pedroso et al. (Sun,) reported a other. AI-enabled electrocardiography improved structural heart disease discrimination (AUC 0.73 vs 0.52) and reduced time to identify 50% of cases from 81 to 19 days versus standard referral.