Back

Machine learning approach to identify clusters of patients with distinct cardiac chronotropic response during incremental exercise

12 April 2026 (08:00 - 15:30)
Organised by: Logo
Congress Presentation Part of: e-Cardiology and digital health Artificial Intelligence EHRA Premium Access EHRA 2026 European Heart Rhythm Association (EHRA)

About the speaker

Photo
Mr Ali Rammal

Pitie Salpetriere APHP University Hospital, Paris (France)

5 More presentations in this session

Presentation thumbnail
Making wrist photoplethysmography reliable for clinical activity quantification: validation of a novel machine learning artefact removal procedure
Miss P. Vermunicht (Antwerp, BE)
Abstract Slides Video
Presentation thumbnail
Bi-atrial synthetic populations with geometry-conditioned conduction velocity for atrial fibrillation simulations
Doctor A. Briosa E Gala (Bern, CH)
Abstract Slides Video
Presentation thumbnail
Atrial/AV parsing and QRS govern clinical usefulness of image-native 12-lead ECG interpretation by a compact multimodal reasoning model
Doctor I. Zeljkovic (Zagreb, HR)
Abstract Slides Video
Presentation thumbnail
Safety-concordant performance of large language models on cardiology vignettes
Doctor A. Jordan (Zagreb, HR)
Abstract Slides Video
Presentation thumbnail
A novel AI-ECG tool for detecting non-pulmonary vein trigger before atrial fibrillation ablation
Professor J. Choi (Seoul, KR)
Abstract Slides Video

Access the full session

Session thumbnail
e-Cardiology and digital health

The Event

Event poster

EHRA 2026

12 April - 14 April 2026
You may be interested in
thumbnail
Congress Session
eCardiology
thumbnail
Congress Presentation
Investigating correlations in sport adaptation using artificial intelligence
thumbnail
Congress Presentation
Unsupervised machine learning of multimodal data to identify atrial fibrillation subgroups likely to benefit from pulmonary vein isolation

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources