ArticlePediatric cardiology2021
Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning.
Article in Pediatric cardiology, 2021. 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.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of fallot in pediatric patients : a feasibility study.Scientific reports · 2026Article
- Machine learning-based predictive model for atrial arrhythmia following transcatheter atrial septal defect closure.International journal of cardiology. Congenital heart disease · 2025Article
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.BMC pediatrics · 2025Article
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Review
- Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.Frontiers in cardiovascular medicine · 2025Article
- Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.Children (Basel, Switzerland) · 2024Review
- Transesophageal echocardiography diagnosis of abnormal left atrium to inferior vena cava communication: A case report.Clinical case reports · 2024Article
- Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.Circulation · 2024Article
- Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.International journal of computer vision · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.EClinicalMedicine · 2023Article
- Artificial intelligence-enabled 8-lead ECG detection of atrial septal defect among adults: a novel diagnostic tool.Frontiers in cardiovascular medicine · 2023Article
- Echocardiography-based AI for detection and quantification of atrial septal defect.Frontiers in cardiovascular medicine · 2023Article
- Data harnessing to nurture the human mind for a tailored approach to the child.Pediatric research · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heart murmur associated with atrial septal defects is often faint and can thus only be detected by chance. Although electrocardiogram examination can prompt diagnoses, identification of specific findings remains a major challenge. We demonstrate improved diagnostic accuracy realized by incorporating a proposed deep learning model, comprising a convolutional neural network (CNN) and long short-term memory (LSTM), with electrocardiograms. This retrospective observational study included 1192 electrocardiograms of 728 participants from January 1, 2000, to December 31, 2017, at Tokyo Women's Medical University Hospital. Using echocardiography, we confirmed the status of healthy subjects-no structural heart disease-and the diagnosis of atrial septal defects in patients. We used a deep learning model comprising a CNN and LTSMs. All pediatric cardiologists (n = 12) were blinded to patient groupings when analyzing them by electrocardiogram. Using electrocardiograms, the model's diagnostic ability was compared with that of pediatric cardiologists. We assessed 1192 electrocardiograms (828 normally structured hearts and 364 atrial septal defects) pertaining to 792 participants. The deep learning model results revealed that the accuracy, sensitivity, specificity, positive predictive value, and F1 score were 0.89, 0.76, 0.96, 0.88, and 0.81, respectively. The pediatric cardiologists (n = 12) achieved means of accuracy, sensitivity, specificity, positive predictive value, and F1 score of 0.58 ± 0.06, 0.53 ± 0.04, 0.67 ± 0.10, 0.69 ± 0.18, and 0.58 ± 0.06, respectively. The proposed method is a superior alternative to accurately diagnose atrial septal defects.
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