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Prediction and classification of aortic stenosis severity based on electrocardiogram with deep learning model

30 March 2025 (08:00 - 15:30)
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Congress Presentation Part of: Artificial intelligence and arrhythmias (1) Artificial Intelligence EHRA Premium Access EHRA 2025 European Heart Rhythm Association (EHRA)

About the speaker

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Associate Professor Ki Hong Lee

Chonnam National University Hospital, Gwangju (Korea (Republic of))

6 More presentations in this session

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Enhancing thromboembolic and major adverse cardiac events risk assessment in atrial fibrillation using AI-driven ECG: a comparative analysis with CHA2DS2-VA scoring
Associate Professor Y. Baek (Incheon, KR)
Abstract Slides Video
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Enhancing atrial fibrillation detection during normal sinus rhythm using self-supervised electrocardiogram foundation model
Associate Professor K. Lee (Gwangju, KR)
Abstract Slides Video
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Beyond human accuracy: application of artificial intelligence for establishing syncope diagnosis in daily practice of the syncope unit
Mr S. van Zanten (Delft, NL)
Abstract Slides Video
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Application of machine learning techniques to optimise for limited datasets in an AI-ECG model for Brugada classification
Doctor K. Saleh (London, GB)
Abstract Slides Video
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Artificial intelligence estimated electrocardiographic age trajectory predicts recurrence after atrial fibrillation catheter ablation
Doctor H. Park (Seoul, KR)
Abstract Slides Video

Access the full session

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Artificial intelligence and arrhythmias (1)

The Event

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EHRA 2025

30 March - 1 April 2025
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novartis Explore sponsored resources