Back

Impact of arrhythmias on the performance of a deep learning model for predicting echocardiographic abnormalities from electrocardiograms

30 August 2025 (09:15 - 10:00)
Organised by: Logo
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

Photo
Doctor Goro Fujiki

The University of Tokyo Hospital, Tokyo (Japan)

6 More presentations in this session

Presentation thumbnail
Artificial intelligence for prediction of major arrhythmic events in dilated cardiomyopathy
Doctor M. Coriano' (London, GB)
Abstract Slides Video
Presentation thumbnail
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
Presentation thumbnail
Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using artificial intelligence-enhanced electrocardiogram
Doctor Z. Xiao (Shanghai, CN)
Abstract Slides Video
Presentation thumbnail
Latent representation of intracardiac electrograms using an AI-based autoencoder for rhythm classification
Mr L. Lin (Madrid, ES)
Abstract Slides Video
Presentation thumbnail
Identifying optimal electrocardiogram lead combinations for artificial intelligence
Mr M. Nakayama (Isehara, JP)
Abstract Slides Video

Access the full session

Session thumbnail
Data meets diagnostics: use of artificial intelligence in arrhythmias

The Event

Event poster

ESC Congress 2025

30 August 2025 09:15 CET
You may be interested in
thumbnail
Congress Session
Spotlight on innovation: top publications in the European Heart Journal - Digital Health 2023-2024
thumbnail
Congress Presentation
Leveraging deep learning for detecting atrial fibrillation and flutter and predicting thromboembolic events from images of 12-lead electrocardiograms in sinus rhythm
thumbnail
Congress Session
Revolutionising cardiac diagnosis: exploring deep learning in electrocardiography and beyond

ESC 365 is supported by

novartis Explore sponsored resources
novo-nordisk Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources
novo-nordisk Explore sponsored resources