Back

Meta-machine learning for enhanced detection of atrial fibrillation after stroke: FIND-AFDAS

29 August 2025 (07:00 - 18:00)
Organised by: Logo
Congress Presentation Part of: Atrial fibrillation and artificial intelligence: the winning couple Stroke Prevention ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

Photo
Doctor Mohammad Haris

University of Leeds, Leeds (United Kingdom of Great Britain & Northern Ireland)

7 More presentations in this session

Presentation thumbnail
Prediction of long-term arrhythmic recurrence and major adverse cardiovascular events using machine learning in patients undergoing catheter ablation for atrial fibrillation: historical cohort study
Doctor T. Uchida (Hofu, JP)
Abstract Slides Video
Presentation thumbnail
A first step towards proactive atrial fibrillation management using ai prediction in a 10-120 minute window
Professor H. Wang (Shenyang, CN)
Abstract Slides Video
Presentation thumbnail
Topological Data Analysis for Patient Stratification and Stroke Risk in Atrial Fibrillation
Doctor E. Shahmohamadi (Adelaide, AU)
Abstract Slides Video
Presentation thumbnail
Prediction of atrial fibrillation over time using artificial intelligence based on ECG features and clinical risk factors
Doctor M. Khan (Stockholm, SE)
Abstract Slides Video
Presentation thumbnail
Statistical shape and appearance modelling of atrial fibrillation: linking left atrial geometry and rotational propagation patterns
Doctor A. Sharp (Oxford, GB)
Abstract Slides Video

Access the full session

Session thumbnail
Atrial fibrillation and artificial intelligence: the winning couple

The Event

Event poster

ESC Congress 2025

29 August - 1 September 2025
You may be interested in
thumbnail
Congress Presentation
Real-world detection of atrial fibrillation after ischaemic stroke using the FIND-AFDAS meta-machine learning algorithm: an unmet need
thumbnail
Congress Session
Digital tools to enhance atrial fibrillation care
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

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources