Work

Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease

Year: 2026

Type: preprint

Abstract: 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 (>50 th 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.

Source: medRxiv

DOI: 10.64898/2026.02.06.26345782