Charitha Reddy, Yi Yan, Min Qiu, Yi Tang, Bo Jin, Zhi Han, Yuhang Li, Sihan Zhou, Qiming Tang, Huan Xiao, Shu Yang, Qigui Wen, Lan-Ping Wu, Li-Jun Fu, Ze-Yu Jing, Yi-Jia Yang, Yu-Qi Zhang, Naoto Ozawa, Takumi Ichikawa, Ellen Ling, Ronald J. Wong, Nima Aghaeepour, Brice Gaudilliere, Martin S. Angst, Karl G. Sylvester, Harvey J. Cohen, Gary L. Darmstadt, Henry Chubb, Scott Ceresnak, Animesh Tandon, Doff B. McElhinney, Seda Tierney, Hao Zhang, Xuefeng B. Ling
Stanford University School of Medicine. Shanghai Children’s Medical Center and Shanghai Jiao Tong University School of Medicine. Chongqing Youyoubaobei Women and Children’s Hospital and Children’s Hospital of Chongqing Medical University. HBI Solutions Inc. Nippon Life Insurance Company. Florida State University. Cleveland Clinic.
United States, China and Japan
Nature Portfolio Journals Digital Medicine
NPJ Digit Med 2025; 8:
DOI: 10.1038/s41746-025-02123-x
Abstract
Congenital and acquired heart disease affects approximately 1% of children worldwide, and right ventricular (RV) dysfunction is a common and complex manifestation in conditions such as congenital heart disease, pulmonary hypertension, and prematurity. Accurate RV assessment remains difficult due to the ventricle’s irregular geometry and morphological variability in pediatric patients. Using 24,984 echocardiograms from 3993 children across four tertiary centers in North America and Asia, we developed and validated a video-based deep learning framework for automated RV functional assessment. The model performs frame-level ventricular segmentation and beat-by-beat estimation of fractional area change (FAC), classification of RV-related disease, and exploratory prediction of left ventricular ejection fraction (LV EF). A U²-Net architecture achieved high segmentation accuracy (Dice = 0.86 [A4C], 0.88 [PSAX]) and classification performance (AUC = 0.95 U.S., 0.97 Asia). In LV EF prediction, the model outperformed previous methods across cohorts. This validated framework enables expert-level, real-time quantification of pediatric ventricular function, enhancing diagnostic consistency, reducing manual workload, and supporting earlier intervention for children with heart disease, particularly in resource-limited settings.
Category
Heart Dysfunction Associated with Pulmonary Vascular Disease (Right)
Diagnostic Testing for Pulmonary Vascular Disease. Non-invasive Testing
Mechanical and Computer Models of Pulmonary Vascular Disease and Therapy
Age Focus: Pediatric Pulmonary Vascular Disease
Fresh or Filed Publication: Fresh (PHresh). Less than 1-2 years since publication
Article Access
Free PDF File or Full Text Article Available Through PubMed or DOI: Yes