Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence

Published in Circulation, 2026

Recommended citation: Platon Lukyanenko, Sunil J Ghelani, Yuting Yang, Bohan Jiang, Timothy A Miller, David Harrild, Nao Sasaki, Francesca Sperotto, Danielle Sganga, John K Triedman, Andrew J Powell, Tal Geva, William G La Cava, and Joshua Mayourian. 2026. Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence. In Circulation. https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.126.079781

Abstract:

BACKGROUND Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo) is central to diagnosing and triaging CHD, yet expert interpretation remains a scarce and maldistributed global resource. Artificial intelligence offers the potential to democratize diagnostics and to extend expert-level interpretation beyond large academic centers, but its application in CHD remains underexplored. METHODS We developed EchoFocus-CHD, an artificial intelligence–enabled model for automated detection of 12 critical and 8 noncritical CHD lesions, individually and as composites. The composite critical CHD outcome was the primary end point. The model expands on a multitask, view-agnostic architecture (PanEcho) with a transformer encoder to improve focus on relevant echo views. The model was internally trained (80%) and tested (20 …