Background Congenital and acquired heart disease affects ~1% of children globally, with right ventricular (RV) dysfunction being a common and complex issue due to conditions like congenital heart disease (CHD), pulmonary hypertension (PH), and prematurity. Accurate RV assessment is challenging due to its unique geometry, interventricular interactions, and morphological variability in pediatric patients. Fractional area change (FAC), a key echocardiographic measure, correlates strongly with disease severity, aiding in timely intervention and prognosis. AI learning shows the potential to automate and standardize RV assessments, overcoming traditional limitations and improving early diagnosis and management of pediatric cardiovascular disorders. Methods Using 24,984 echocardiograms from 3,993 pediatric patients across four tertiary care centers (one in North America, three in Asia), we developed and validated an AI framework for automated RV assessment. The framework employs multi-task learning to perform ventricular segmentation, beat-by-beat quantification of RV FAC, and identification of cardiac abnormalities like PH. It was also extended to enhance left ventricular (LV) functional assessment. Findings Our AI system achieved Dice similarity coefficients of 0.86 (apical-four-chamber, A4C) and 0.88 (parasternal-short-axis, PSAX) for RV segmentation, matching expert annotations. It demonstrated robust RV functional assessment, with AUCs of 0.95 (U.S. cohort) and 0.97 (Asian cohort). For PH classification, diagnostic accuracies were 0.95 (U.S.) and 0.94 (Asian), confirming consistent performance across populations. When extended to LV assessment, the framework significantly improved LV ejection fraction (EF) prediction in both U.S. and Asian cohorts. Interpretation This validated AI framework enables reliable, automated ventricular function analysis, matching expert-level performance. By enhancing clinical workflows and standardizing pediatric cardiac assessments, it has the potential to improve care management for pediatric cardiovascular disorders, particularly in resource-limited settings. Funding This work was supported by the U.S. NIH 1R41HL160362-01 to XBL and K23HL150279 to AT.

BJ, YL, QT, and SZ are employees of HBI Solutions Inc. NO and TI are Stanford University visiting scholars from Nippon Life Insurance Company. However, the companies had no role in the design of the study, data collection, data analysis, interpretation, or the preparation of this manuscript. All research and experimental work were conducted independently, and no external influence impacted on the study's findings or conclusions. AT reports consulting fees from Siemens Healthineers for work unrelated to echocardiography. The other authors associated with hospitals and universities declare no competing interest.

This work was supported by the U.S. NIH 1R41HL160362-01 to XBL and K23HL150279 to AT.

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The study protocol was reviewed and approved by the Institutional Review Boards of all participating hospitals (Stanford Children's Hospital, the Children's Hospital of Chongqing Medical University, Chongqing YouYou BaoBei Women's and Children's Hospital, and Shanghai Children's Medical Center), ensuring strict adherence to institutional guidelines. To safeguard patient privacy and confidentiality, comprehensive data protection measures were implemented. These included the removal of personal identifiers, secure file format conversion, and thorough manual dataset review. These rigorous procedures ensured compliance with ethical standards for managing sensitive medical information.

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The data and code used in this work will be publicly available on GitHub (https://github.com/bxlinglaboratory/EchoRV) upon publication.