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An explainable AI for trustworthy detection of atrial fibrillation on reduced lead ECGs in mobile applications

1 September 2024 (15:00 - 15:50)
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Congress Presentation Part of: Progress and challenges in artificial intelligence for cardiovascular risk assessment and diagnosis Artificial Intelligence ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

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Mr Alexander Hammer

Dresden University Of Technology, Dresden (Germany)

8 More presentations in this session

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Importance and limits of cardiovascular risk factors on the prediction of SCD using machine learning approach.
Professor X. Jouven (Paris, FR)
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Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification
Mr P. Mohammadi Kazaj (Bern, CH)
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Preventing unnecessary echocardiograms in de novo patients referred to the cardiology outpatient clinic using electrocardiogram-based deep learning
Mr B. Arends (Utrecht, NL)
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Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain
Mr B. Arends (Utrecht, NL)
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Predicting mortality of type-2 diabetes mellitus by applying machine learning to electronic medical records in the west of scotland and hong kong
Miss N. Kaur (Glasgow, GB)
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Access the full session

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Progress and challenges in artificial intelligence for cardiovascular risk assessment and diagnosis

The Event

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ESC Congress 2024

1 September 2024 15:00 CET
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ESC 365 is supported by

novartis Explore sponsored resources