Back

Artificial intelligence for prediction of major arrhythmic events in dilated cardiomyopathy

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

About the speaker

Photo
Doctor Mattia Coriano'

Imperial College London, London (United Kingdom of Great Britain & Northern Ireland)

6 More presentations in this session

Presentation thumbnail
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
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 (Boston, US)
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

29 August - 1 September 2025
You may be interested in
thumbnail
Congress Presentation
Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data
thumbnail
Congress Presentation
Non-invasive risk assessment and prediction of cardiac outcomes in patients with heart failure or myocardial infarction using deep learning method
thumbnail
Congress Presentation
Machine learning does not improve risk prediction in heart failure in comparison to the PREDICT-HF model: derivation and validation in PARADIGM-HF and ATMOSPHERE

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources