ArticleJACC. Clinical electrophysiology2025
Expert-Level Automated Diagnosis of the Pediatric ECG Using a Deep Neural Network.
Article in JACC. Clinical electrophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Artificial Intelligence Enhanced Electrocardiogram Analysis for Age and Sex Classification in Youth.Pediatric cardiology · 2026Article
- Age-dependent Dynamics of the Electrocardiographic Parameters in Cardiovascular Disease-Free Children.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.Frontiers in pediatrics · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
backgroundDisparate access to expert pediatric cardiologist care and interpretation of electrocardiograms (ECGs) persists worldwide. Artificial intelligence-enhanced ECG (AI-ECG) has shown promise for automated diagnosis of ECGs in adults but has yet to be explored in the pediatric setting.
objectivesThis study sought to determine whether an AI-ECG model can accurately perform automated diagnosis of pediatric ECGs.
methodsThis retrospective single-center cohort study included all patients with an ECG at Boston Children's Hospital read by an experienced pediatric cardiologist (≥5,000 reads) between 2000 and 2022. A convolutional neural network was trained (75% of patients) and internally tested (25% of patients) on ECGs to predict ECG diagnoses. The primary outcome was a composite of any ECG abnormality (ie, detecting normal vs abnormal ECG). Secondary outcomes include Wolff-Parkinson-White syndrome (WPW) and prolonged QTc. Model performance was assessed with area under the receiver-operating (AUROC) and precision recall (AUPRC) curves.
resultsThe main cohort consisted of 201,620 patients (49% male; 11% with known congenital heart disease) and 583,134 ECGs (median age 11.7 years [Q1-Q3: 3.1-16.9 years]; 56% any ECG abnormality, 1.0% WPW, and 5.3% with prolonged QTc). The AI-ECG model outperformed the commercial software interpretations for detecting any abnormality (AUROC 0.94; AUPRC 0.96), WPW (AUROC 0.99; AUPRC 0.88), and prolonged QTc (AUROC 0.96; AUPRC 0.63). During readjudication of ECGs with AI-ECG/original cardiologist read discordance, blinded expert readers were more likely to agree with AI-ECG classification than the original reader to detect any abnormality (P = 0.001), WPW (P = 0.01), and prolonged QTc (P = 0.07).
conclusionsOur model provides expert-level automated diagnosis of the pediatric 12-lead ECG, which may improve access to care.
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