ArticleJournal of the American Heart Association2024
Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.
Article in Journal of the American Heart Association, 2024. 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.
- Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries.Pediatric cardiology · 2026Article
- A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel Electrocardiogram Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2026Article
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.The Lancet. Digital health · 2025Article
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
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Authors and funding
14 authors.
Funding
Abstract
backgroundEarly detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12-lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence-enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. METHODS AND
resultsWe identified 10 142 unique pediatric patients (age≤18) with a 10-second, 12-lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89-0.98) and 0.88 (95% CI, 0.83-0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data-derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84-0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87-0.94).
conclusionsAn artificial intelligence-enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult-trained models offer good performance, improvements are seen when training pediatric-specific models.
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