Back

Left atrial function for predicting atrial fibrillation: a machine learning approach

2 September 2024 (10:00 - 10:50)
Organised by: Logo
Congress Presentation Part of: Prediction of incident atrial fibrillation: from biomarkers to artificial intelligence Prevalence and Incidence of Atrial Fibrillation ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

Photo
Doctor Gabriele Masini

University of Pisa, Pisa (Italy)

6 More presentations in this session

Presentation thumbnail
Impact of changes in proteinuria on the risk of atrial fibrillation in patients with diabetes mellitus: a nationwide population-based study
Doctor H. Rha (Seoul, KR)
Abstract Slides Video
Presentation thumbnail
Proteomic and genomic evaluation of plasma protein biomarkers for atrial fibrillation
Mr O. Vad (Copenhagen, DK)
Abstract Slides Video
Presentation thumbnail
Circulating ketone bodies, genetic predisposition, and incident atrial fibrillation
Ms Y. Sun (Shanghai, CN)
Abstract Slides Video
Presentation thumbnail
Polygenic risk score for prediction of atrial fibrillation according to presence of the hypertrophic cardiomyopathy
Doctor B. Joung (Seoul, KR)
Abstract Slides Video
Presentation thumbnail
Proteinuria and the risk of incident atrial fibrillation according to diabetes stage in patients with diabetes: a nationwide population-based cohort study
Mr M. Yeo (Seoul, KR)
Abstract Slides Video

Access the full session

Session thumbnail
Prediction of incident atrial fibrillation: from biomarkers to artificial intelligence

The Event

Event poster

ESC Congress 2024

2 September 2024 10:00 CET
You may be interested in
thumbnail
Congress Presentation
Predicting incident atrial fibrillation using AI-ECG: enhancing risk stratification for stroke prevention
thumbnail
Congress Presentation
Explainable machine learning models to improve prediction of incident stroke in atrial fibrillation patients using health records, medical imaging and ECG derived metrics
thumbnail
Congress Presentation
Machine learning-lased prediction of atrial fibrillation in patients with atrial high-rate episodes

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources