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TRV-2026-0774Certified recordPeer-reviewed

The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease

Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate t…

Health · The Trace — both readings · certified 2026-08-15 · v1 · article view · machine-readable

Current reading — gain

Deep learning models applied to standard ECGs can accurately identify arrhythmias, ventricular dysfunction, and congenital heart disease in pediatric populations, supporting earlier detection and risk stratification.

Current reading — problem

Most AI-ECG studies in pediatric and congenital heart disease remain retrospective and single-center with limited external validation, facing challenges from small datasets and age-dependent ECG variation.

What this doesn’t fix

Most evidence is retrospective and single-center with limited external validation, constrained by small datasets and age-dependent physiologic heterogeneity.

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Truvace Impact Record TRV-2026-0774, v1: “The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease.” Truvace, 2026-08-15. /record/TRV-2026-0774 (accessed at citation time). sha256 c724ac15e9948bfa…

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