ArticleEuropean heart journal2025
Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.
Article in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Repaired tetralogy of Fallot across adulthood: from right ventricular remodelling to targeted intervention.Heart failure reviews · 2026Review
- Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.European heart journal. Digital health · 2026Article
- Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.Scientific reports · 2026Article
- Development and evaluation of an artificial intelligence-based electrocardiogram prediction model for emergency chest pain patients.Frontiers in medicine · 2026Article
- Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.BioData mining · 2025Article
- Current State of Artificial Intelligence in Assessing Cardiac Function.Current cardiology reports · 2025Review
- Prediction of Postoperative Mortality After Fontan Procedure: A Clinical Prediction Model Study Using Deep Learning Artificial Intelligence Techniques.Journal of cardiovascular development and disease · 2025Article
- Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.Scientific reports · 2025Article
- Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes.European heart journal. Digital health · 2025Article
- Right ventricular dysfunction following tetralogy of Fallot correction: anatomical determinants and therapeutic strategies.International journal of surgery (London, England) · 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
- Expanding artificial intelligence to understudied populations: congenital heart disease as the next frontier.European heart journal · 2025Article
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Detection of Deaths Caused by Hyperkalemia.Biomedicines · 2025Review
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND AND
aimsRobust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool across the lifespan of a large, diverse cohort with CHD.
methodsA convolutional neural network was trained (50%) and tested (50%) on ECGs obtained in cardiology clinic at the Boston Children's Hospital to detect 5-year mortality. Temporal validation on a contemporary cohort was performed. Model performance was evaluated using the area under the receiver operating characteristic and precision-recall curves.
resultsThe training and test cohorts composed of 112 804 ECGs (39 784 patients; ECG age range 0-85 years; 4.9% 5-year mortality) and 112 575 ECGs (39 784 patients; ECG age range 0-92 years; 4.6% 5-year mortality from ECG), respectively. Model performance (area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.77-0.81; area under the precision-recall curve 0.17, 95% confidence interval 0.15-0.19) outperformed age at ECG, QRS duration, and left ventricular ejection fraction and was similar during temporal validation. In subgroup analysis, artificial intelligence-enhanced ECG outperformed left ventricular ejection fraction across a wide range of CHD lesions. Kaplan-Meier analysis demonstrates predictive value for longer-term mortality in the overall cohort and for lesion subgroups. In the overall cohort, precordial lead QRS complexes were most salient with high-risk features including wide and low-amplitude QRS complexes. Lesion-specific high-risk features such as QRS fragmentation in tetralogy of Fallot were identified.
conclusionsThis temporally validated model shows promise to inexpensively risk-stratify individuals with CHD across the lifespan, which may inform the timing of imaging/interventions and facilitate improved access to care.
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