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Updated: Jun 20, 2026
08:17
Murine Echocardiography of Left Atrium, Aorta, and Pulmonary Artery
Published on: February 20, 2017
Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease
Yuting Yang1,2, Lorenzo Peracchio3, Joshua Mayourian2,4
1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Summary
Artificial intelligence-enhanced electrocardiograms (AI-ECG) can detect heart dysfunction in children. A new method improves AI-ECG accuracy with limited data, making it suitable for hospitals with fewer resources.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Artificial intelligence-enhanced electrocardiogram (AI-ECG) shows potential for non-invasive screening of pediatric congenital heart disease.
- Current AI-ECG methods require large labeled datasets, limiting their use in resource-limited hospitals with scarce pediatric ECG data.
Purpose of the Study:
- To develop a robust training framework for AI-ECG to enhance performance in low-resource settings.
- To enable reliable and low-cost detection of left ventricular systolic dysfunction using AI-ECG in pediatric populations.
Main Methods:
- An on-manifold adversarial perturbation strategy was used to generate synthetic pediatric ECG samples reflecting real-world variations.
- An architecture-agnostic, uncertainty-aware adversarial training algorithm was developed to improve model robustness.
Main Results:
- The proposed method demonstrated effective low-cost and reliable detection of left ventricular systolic dysfunction.
- Performance was validated on large-scale pediatric (n=178,495) and adult (n=100,000) datasets.
Conclusions:
- The developed AI-ECG framework improves performance under low-resource conditions, addressing a key limitation in pediatric cardiology.
- This approach holds significant potential for deployment in resource-limited clinical settings for early detection of heart conditions.
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