ArticleJournal of the American College of Cardiology2024
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.
Article in Journal of the American College of Cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Repaired tetralogy of Fallot across adulthood: from right ventricular remodelling to targeted intervention.Heart failure reviews · 2026Review
- Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.Indian pediatrics · 2026Article
- Automated assessment of right heart function by artificial intelligence: A systematic review and meta-analysis.European journal of radiology open · 2026Article
- An ECG foundation model for generalizable cardiac function prediction across the lifespan.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning predicts stent implantation in borderline coronary lesions from angiography.NPJ digital medicine · 2026Article
- Automated assessment of right ventricular systolic function from coronary angiograms with video-based artificial intelligence algorithms: development, validation, comparison against humans, and prospective deployment.European heart journal. Digital health · 2026Article
- Explainable machine learning model for identifying key risk factors in congenital heart disease prediction using questionnaire data: a retrospective case-control study.BMC medical informatics and decision making · 2026Article
- Comparative evaluation of multimodal large language models for diagnostic accuracy in pediatric electrocardiography: a prospective comparative diagnostic accuracy study.European journal of pediatrics · 2026Article
- Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries.Pediatric cardiology · 2026Article
- Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.European heart journal. Digital health · 2026Article
- Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial Electrocardiograms.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- A Paradigm Shift in Congenital Heart Disease: A Scientometric Portrait of the Rise of Computational Intelligence.Pediatric cardiology · 2025Review
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
- Right ventricular dysfunction following tetralogy of Fallot correction: anatomical determinants and therapeutic strategies.International journal of surgery (London, England) · 2025Review
- Expert-Level Automated Diagnosis of the Pediatric ECG Using a Deep Neural Network.JACC. Clinical electrophysiology · 2025Article
- 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
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundArtificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).
objectivesThe purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD.
methodsWe trained (80%) and tested (20%) a convolutional neural network on paired ECG-CMRs (≤30 days apart) from patients with and without CHD to detect left ventricular (LV) dysfunction (ejection fraction ≤40%), RV dysfunction (ejection fraction ≤35%), and LV and RV dilation (end-diastolic volume z-score ≥4). Performance was assessed during internal testing and external validation on an outside health care system using area under receiver-operating curve (AUROC) and area under precision recall curve.
resultsThe internal and external cohorts comprised 8,584 ECG-CMR pairs (n = 4,941; median CMR age 20.7 years) and 909 ECG-CMR pairs (n = 746; median CMR age 25.4 years), respectively. Model performance was similar for internal testing (AUROC: LV dysfunction 0.87; LV dilation 0.86; RV dysfunction 0.88; RV dilation 0.81) and external validation (AUROC: LV dysfunction 0.89; LV dilation 0.83; RV dysfunction 0.82; RV dilation 0.80). Model performance was lowest in functionally single ventricle patients. Tetralogy of Fallot patients predicted to be at high risk of ventricular dysfunction had lower survival (P < 0.001). Model explainability via saliency mapping revealed that lateral precordial leads influence all outcome predictions, with high-risk features including QRS widening and T-wave inversions for RV dysfunction/dilation.
conclusionsAI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.
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