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Identifying optimal electrocardiogram lead combinations for artificial intelligence

29 August 2025 (07:00 - 18:00)
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Congress Presentation Part of: Data meets diagnostics: use of artificial intelligence in arrhythmias Deep Learning ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

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Mr Masamitsu Nakayama

Brigham and Women's Hospital, Harvard Medical School, Boston (United States of America)

6 More presentations in this session

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Artificial intelligence for prediction of major arrhythmic events in dilated cardiomyopathy
Doctor M. Coriano' (London, GB)
Abstract Slides Video
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Impact of arrhythmias on the performance of a deep learning model for predicting echocardiographic abnormalities from electrocardiograms
Doctor G. Fujiki (Tokyo, JP)
Abstract Slides Video
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Development of deep learning models for predicting atrial fibrillation occurrence using real world hand-held mobile electrocardiograms
Mr Y. Na (Seoul, KR)
Abstract Slides Video
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Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using artificial intelligence-enhanced electrocardiogram
Doctor Z. Xiao (Shanghai, CN)
Abstract Slides Video
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Latent representation of intracardiac electrograms using an AI-based autoencoder for rhythm classification
Mr L. Lin (Madrid, ES)
Abstract Slides Video

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Data meets diagnostics: use of artificial intelligence in arrhythmias

The Event

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ESC Congress 2025

29 August - 1 September 2025
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novartis Explore sponsored resources