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Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects
Joshua Mayourian1,2, Robert Geggel1,2, William G La Cava1,2
1Department of Cardiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Pediatric Cardiology
|July 2, 2024
Summary
Artificial intelligence-enhanced electrocardiogram (AI-pECG) shows promise for detecting secundum atrial septal defects (ASD2) in children. This AI tool can help in early screening and diagnosis of ASD2 in pediatric patients.
Area of Science:
- Cardiology
- Artificial Intelligence
- Pediatric Health
Background:
- Secundum atrial septal defect (ASD2) detection in children is often delayed, leading to potential complications.
- Artificial intelligence (AI) has shown promise in adult ECG analysis for ASD2 detection, but pediatric applications are underexplored.
Purpose of the Study:
- To develop and evaluate an AI-pECG model for detecting ASD2 in pediatric patients (≤18 years old).
- To assess the model's performance in internal testing and emergency department cohorts.
Main Methods:
- A convolutional neural network (AI-pECG) was trained on paired ECG-echocardiograms (≤2 days apart) from pediatric patients.
- Model performance was evaluated using AUROC and AUPRC on internal testing and emergency department cohorts.
Main Results:
- The AI-pECG model demonstrated strong performance in both cohorts (Internal Test: AUROC 0.84, AUPRC 0.46; ED Cohort: AUROC 0.80, AUPRC 0.30).
- AI-pECG outperformed traditional ECG findings of incomplete right bundle branch block.
- Explainability analysis identified key ECG features associated with ASD2 risk.
Conclusions:
- AI-pECG shows significant promise for inexpensive screening and detection of ASD2 in pediatric patients.
- Further multicenter validation and prospective trials are needed to integrate AI-pECG into clinical decision-making.
