ArticleNature communications2024
Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Beyond Accuracy: Defining the Role of Artificial Intelligence in Pediatric Electrocardiography.Indian pediatrics · 2026Article
- Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.Indian pediatrics · 2026Article
- Artificial Intelligence in Pediatric Cardiovascular Genetics: From Multimodal Diagnosis to Risk Prediction and Precision Therapeutics.International journal of molecular sciences · 2026Review
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Hybrid vision transformer framework for congenital heart disease diagnosis.Scientific reports · 2026Article
- Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries.Pediatric cardiology · 2026Article
- A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.Scientific reports · 2026Article
- MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations.Scientific data · 2026Article
- Age-dependent Dynamics of the Electrocardiographic Parameters in Cardiovascular Disease-Free Children.medRxiv : the preprint server for health sciences · 2026Article
- ECG Criteria and Diagnostic Score for Right Atrial Disease in Children: The CHILDHEART Study.Pediatric cardiology · 2026Article
- Unveiling Hearts: Deep Learning-Based Electrocardiogram Classification for Congenital Heart Disease Detection.Current medical science · 2025Article
- AI-enhanced flexible ECG patch for accurate heart disease diagnosis, optimal wear positioning, and interactive medical consultation.National science review · 2025Article
- Infant Electrocardiogram-Based Deep Learning Predicts Critical Congenital Heart Disease.JACC. Clinical electrophysiology · 2025Article
- A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis.medRxiv : the preprint server for health sciences · 2025Article
- A performance analysis of convolutional autoencoder modified WaveGAN architectures for realistic 12 lead electrocardiogram synthesis.Scientific reports · 2025Article
- Green Synthesis of Biochar-Supported Nanoscale Zero-Valent Iron Using Tea Polyphenol for Efficient Cadmium Immobilization in Soil.Nanomaterials (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.