Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence

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.

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%) on the first echo per patient from Boston Children's Hospital, with further evaluation on a referral cohort of echo studies performed at external US and international centers.

The internal and referral cohorts included 3.4 million videos from 54 727 echos (median age at echo, 7.1 years [interquartile range, 0.2-15.0 years]; 5.8% critical CHD, 23.6% noncritical CHD) and 167 484 videos from 3356 echos (median age at echo, 2.5 years [interquartile range, 0.3-9.4 years]; 29.4% critical CHD, 45.6% noncritical CHD), respectively. EchoFocus-CHD showed excellent internal ability to detect the composite critical CHD outcome (area under the receiver-operating curve [AUROC], 0.94; positive likelihood ratio, 7.50; negative likelihood ratio, 0.14) and individual critical lesions (AUROC, 0.83-1.00), as well as composite noncritical CHD (AUROC, 0.90; positive likelihood ratio, 5.00; negative likelihood ratio, 0.23) and individual noncritical lesions (AUROC, 0.70-0.96). Performance declined during evaluation on the referral cohort to detect critical CHD (AUROC, 0.77), coinciding with greater expert disagreement on referral cases (κ=0.72 versus 0.82 for internal cases). Explainability analyses demonstrated that the model prioritized the same clinically relevant views (parasternal long axis, parasternal short axis, subxiphoid long axis, apical) across internal and referral cohorts, whereas uniform manifold approximation and projection analysis revealed a domain shift between cohorts. Retraining on all available US patients attenuated domain shift effects, improving international critical CHD detection (AUROC, 0.87) and calibration.

EchoFocus-CHD shows promise for automated CHD detection to advance equitable global cardiovascular care and highlights the need to address domain shift and to establish external validation before real-world deployment.