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

1 September 2025 (14:15 - 15:00)
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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

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Doctor Mohammad Haris

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

7 More presentations in this session

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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
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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
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Topological Data Analysis for Patient Stratification and Stroke Risk in Atrial Fibrillation
Doctor E. Shahmohamadi (Adelaide, AU)
Abstract Slides Video
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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
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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

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Atrial fibrillation and artificial intelligence: the winning couple

The Event

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ESC Congress 2025

1 September 2025 14:15 CET
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ESC 365 is supported by

novartis Explore sponsored resources