Key result
AI shows strong promise for automated pediatric ECG interpretation but requires prospective multicenter validation.
Why the study?
Interpretation of pediatric ECGs is challenging due to age-related variability and cardiovascular maturation, and AI offers new possibilities for automated analysis and detection of subtle patterns.
Artificial intelligence shows significant promise as a clinical decision-support tool to expand the diagnostic and prognostic capabilities of pediatric electrocardiography, though prospective, externally validated studies are still needed.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“Early detection of LVSD and RVSD in children can lead to initiation of medical therapy that improves heart failure symptoms and reduces mortality rate. The artificial intelligence-enabled ECG algorithms we developed demonstrate accurate detection for both LVSD and RVSD in pediatric patients.”
“We've historically viewed the ECG as a tool for evaluating heart rhythm and blood flow disturbances. However, AI is allowing doctors to uncover patterns that reflect underlying myocardial function, sometimes even before the disease is starting to show sign on an echocardiogram. That has the potential to fundamentally change how we think about one of cardiology's most familiar diagnostic tests.”
“As pediatric cardiology encompasses a heterogeneous group of CHDs with various treatment modalities and often more than one standard treatment approach for the same CHD based on clinician and institutional preferences, generating sufficient datasets to train and validate AI models is challenging. The only solution to this hurdle is a multicenter and international collaboration to design and perform AI-related studies.”
AI may aid pediatric ECG interpretation; leaves open clinical adoption pending prospective multicenter validation.
Introduction Electrocardiography (ECG) remains a fundamental diagnostic tool in pediatric cardiology; however, its interpretation is challenging because normal electrocardiographic parameters vary considerably with age and cardiovascular maturation. The development of artificial intelligence (AI), particularly machine learning and deep learning, has created new possibilities for automated ECG analysis and the detection of subtle patterns that may be difficult to identify using conventional interpretation. This review aims to summarize current applications of AI in pediatric electrocardiography, with particular emphasis on diagnostic and predictive applications and their clinical potential. Current State of Knowledge Current evidence indicates that AI-based models can support the automated interpretation of pediatric ECGs and the detection of rhythm and conduction disorders, including Wolff-Parkinson-White syndrome, long QT syndrome, and congenital heart disease. Deep learning models have also demonstrated the ability to estimate corrected QT intervals and to identify ECG patterns associated with left ventricular dysfunction, hypertrophy, and dilation. Beyond conventional diagnosis, AI-ECG has shown potential for prognostic applications, including the prediction of adverse cardiovascular events in children. These findings suggest that AI models can extract complex information from ECG recordings beyond individual conventional parameters. However, the available evidence remains limited by relatively small and heterogeneous pediatric datasets, age-related variability in ECG characteristics, data heterogeneity, limited external validation, and the predominance of retrospective studies. Moreover, evidence regarding the impact of AI-ECG on clinical decision-making and patient outcomes remains limited. Conclusions AI represents a promising approach to expanding the diagnostic and prognostic potential of pediatric electrocardiography. Current findings support its role as a tool for automated ECG interpretation, disease detection, risk stratification, and indirect assessment of cardiac structure and function. Nevertheless, AI-ECG should currently be regarded as a clinical decision-support tool rather than a replacement for conventional cardiac assessment. Further multicenter, prospective, and externally validated studies are needed to establish the generalizability, safety, interpretability, and clinical utility of AI-based ECG models in pediatric practice.
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Ostrowicz et al. (2026) conducted a review in Pediatric cardiovascular conditions (e.g., rhythm disorders, long QT syndrome, congenital heart disease). Artificial Intelligence (AI) and Deep Learning ECG analysis vs. Conventional electrocardiographic interpretation or algorithms was evaluated. Artificial intelligence models demonstrate high potential for automated pediatric ECG interpretation, disease detection, and risk stratification, though further prospective, multicenter validation is required.
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