Development of an AI based automated analysis of pediatric Apple Watch iECGs.
Apple Watch
CNN
ECG
pediatric cardiology
smart living
Journal
Frontiers in pediatrics
ISSN: 2296-2360
Titre abrégé: Front Pediatr
Pays: Switzerland
ID NLM: 101615492
Informations de publication
Date de publication:
2023
2023
Historique:
received:
13
03
2023
accepted:
26
04
2023
medline:
26
6
2023
pubmed:
26
6
2023
entrez:
26
6
2023
Statut:
epublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 37360371
doi: 10.3389/fped.2023.1185629
pmc: PMC10286860
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1185629Informations de copyright
© 2023 Teich, Franke, Michaelis, Dähnert, Gebauer, Markel and Paech.
Déclaration de conflit d'intérêts
This study received funding from the Leipzig Heart Institute GmbH. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
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