A Multitask Deep Learning Model for Pediatric Echocardiography Analysis

Abstract

BACKGROUND: Congenital heart defects afflict ≈1% of all births worldwide. Although deep learning has shown significant promise in automating and improving adult echocardiography analysis, existing pediatric-based models are often limited to single tasks and specific echocardiographic views. To address this, we introduce EchoAI-Peds, a multitask deep learning model for pediatric echocardiography. Our model was developed using the most comprehensive set of pediatric labels to date and is designed to integrate information from multiple views simultaneously. METHODS: We trained a video-based vision transformer to simultaneously detect 28 congenital heart defects, structural and functional abnormalities, repairs, and interventions directly from complete pediatric echocardiography studies with multiple videos. Our model was developed using >700 000 videos derived from >12 000 studies performed at Stanford Medicine between 2014 and 2021. Specifically, our model was trained on 10 815 studies (median age, 8 [IQR 2–14] years; 45% girls) and validated on 1294 studies (median age, 9 [IQR 2–15] years; 46% girls). Model efficacy was tested on an internal held-out data set of 1336 studies from that same period. In addition, model generalizability was tested on a spatially and temporally distinct patient cohort at the Children’s Hospital of Philadelphia using 2121 studies performed between 2024 and 2025. RESULTS: Our model achieved macroaveraged (area under the receiver operating characteristic curve (AUROC) values of 0.91 (95% CI, 0.90–0.92) on the internal test set (median age, 8 [IQR 2–14] years; 47% girls) and AUROC values of 0.89 (95% CI, 0.88–0.90) on the external test set (median age 6 [IQR 0.92–13] years; 44.4% girls). Moreover, EchoAI-Peds significantly outperformed adult-based echocardiography foundation models trained on substantially larger data sets, including EchoCLIP (internal AUROC=0.58, 95% CI, 0.56–0.60; external AUROC=0.61, 95% CI, 0.59–0.62) and EchoPrime (internal AUROC=0.58, 95% CI, 0.57–0.61; external AUROC=0.60, 95% CI, 0.59–0.62). Finally, our model demonstrated robust performance across patient age, patient sex, and studies with varying number of videos. CONCLUSIONS: Our findings demonstrate the remarkable potential for multitask deep learning models to aid the interpretation of pediatric echocardiograms. In addition, our results underscore the need for models that are specifically tailored to pediatric populations.