Key result
Novel AI algorithm improves pediatric Apple Watch iECG sinus rhythm classification to ~67% sensitivity.
Why the study?
Automatic heart rhythm classification on the Apple Watch does not provide satisfactory results in children, limiting pediatric Apple Watch iECG interpretation to pediatric cardiologists.
Does a self-developed AI algorithm accurately classify normal sinus rhythm on Apple Watch iECGs in pediatric patients compared to 12-lead ECG evaluation by a pediatric cardiologist?
Cross-Sectional (n=48)
Single-blind
No
Does a self-developed AI algorithm accurately classify normal sinus rhythm on Apple Watch iECGs in pediatric patients compared to 12-lead ECG evaluation by a pediatric cardiologist?
A newly developed AI algorithm for pediatric Apple Watch iECGs achieved 96.7% specificity and 66.7% sensitivity for classifying normal sinus rhythm, providing a basis for further development.
Supports AI refinement for pediatric wearable ECGs; leaves open prospective validation before clinical adoption.
Introduction: The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study. Methods: A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI. Results: The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm. Conclusion: The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.
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Teich et al. (2023) conducted a cross-sectional in Pediatric arrhythmias and congenital heart disease (n=48). AI-based algorithm (Xception_v8) vs. Original Apple Watch software was evaluated on Sensitivity for classifying a normal sinus rhythm (p=0.004). A newly developed AI-based algorithm significantly improved the automated classification of normal sinus rhythm in pediatric Apple Watch iECGs compared to the original software, achieving 66.7% sensitivity and 96.7% specificity (p=0.004).
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