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

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

Session: Artificial intelligence and arrhythmias (1)

Topic: Artificial Intelligence

Speaker: Associate Professor K. Lee (Gwangju, KR)

Event: EHRA 2025

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About the speaker

Associate Professor Ki Hong Lee

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

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6 more presentations in this session

Enhancing thromboembolic and major adverse cardiac events risk assessment in atrial fibrillation using AI-driven ECG: a comparative analysis with CHA2DS2-VA scoring

Speaker: Associate Professor Y. Baek (Incheon, KR)

Enhancing atrial fibrillation detection during normal sinus rhythm using self-supervised electrocardiogram foundation model

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Beyond human accuracy: application of artificial intelligence for establishing syncope diagnosis in daily practice of the syncope unit

Speaker: Mr S. van Zanten (Delft, NL)

Application of machine learning techniques to optimise for limited datasets in an AI-ECG model for Brugada classification

Speaker: Doctor K. Saleh (London, GB)

Artificial intelligence estimated electrocardiographic age trajectory predicts recurrence after atrial fibrillation catheter ablation

Speaker: Doctor H. Park (Seoul, KR)

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Artificial intelligence and arrhythmias (1)

Speakers: Associate Professor K. Lee, Associate Professor Y. Baek, Associate Professor K. Lee, Mr S. van Zanten, Doctor K. Saleh...

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EHRA 2025

30 March - 1 April 2025

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