Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease
Journal:
medRxiv
Published Date:
Feb 9, 2026
Abstract
Background: Echocardiography (echo) is a cornerstone of pediatric cardiology, yet access to expert interpreters is limited worldwide, particularly in low-resource and rural settings. Artificial intelligence (AI) offers a mechanism to broadly deliver expert-level precision and standardize measurements, yet AI for comprehensive automated measurements in pediatric and congenital heart disease (CHD) echo remains underdeveloped. Methods: We created EchoFocus-Measure, an AI platform that automatically extracts 18 quantitative parameters and 10 qualitative assessments from full echo studies. The method extends a multi-task, view-agnostic architecture (PanEcho) with a study-level transformer to prioritize diagnostically informative views. Training (80%) and internal testing (20%) were performed on echos from Boston Children's Hospital (BCH), with external evaluation on outside referral studies. Left ventricular ejection fraction (LVEF) was the primary endpoint. Results: The internal cohort included 11.4 million videos from 217,435 echos (60,269 patients; median age 8.5 years; median LVEF 61%), and external validation encompassed 289,613 videos from 3,096 echos (2,506 patients; median age 3.5 years; median LVEF 62%). For LVEF, EchoFocus-Measure exhibited a median absolute error (MAE) of 2.8% internally and 3.8% externally, maintaining accuracy across infants (MAE 3.2%) and complex CHD lesions (e.g., MAE 4.0% for L-loop transposition of the great arteries). EchoFocus-Measure improved upon the PanEcho benchmark (MAE 7.5% for infants; 13.1% for L-loop transposition). Discrepant case (>50th percentile error) adjudication of LVEF demonstrated that model errors (MAE 2.4%) were within human variability (MAE 3.7%). For qualitative measures, EchoFocus-Measure performed well internally (AUROC 0.88-0.95) and modestly externally (AUROC 0.73-0.86). Explainability analyses highlighted model focus on clinically appropriate echo views for LVEF estimation (apical four-chamber, parasternal short/long) and mitral regurgitation assessment (apical four-chamber color Doppler, parasternal short/long color Doppler). Conclusions: EchoFocus-Measure delivers rapid and reliable automated echo measurements across ages and lesions within diverse internal and real-world external cohorts, serving as a step toward scalable, global access to high-quality pediatric cardiovascular care.
