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Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data

30 August 2024 (11:00 - 11:50)
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Congress Presentation Part of: Machine-learning approaches for the diagnosis of heart failure Diagnostic Methods ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

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Doctor Ewa Sieliwonczyk

University Hospital Antwerp, Edegem (Belgium)

7 More presentations in this session

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Long-term clinical outcomes informed by artificial intelligence-enabled single-lead ECG detection of reduced ejection fraction
Mr A. Alrumayh (London, GB)
Abstract Slides Video
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Machine learning methods for diagnostic disease prediction and treatment decisions in parvovirus B19 positive viral cardiomyopathy
Doctor C. Baumeier (Berlin, DE)
Abstract Slides Video
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Construction of an inflammatory, nutritional, and metabolic prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning-based approach
Doctor J. Feng (Singapore, SG)
Abstract Slides Video
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Artificial intelligence to detect pulmonary congestion on chest computed tomography in consecutive dyspnoeic patients
Professor O. Nielsen (Copenhagen, DK)
Abstract Slides Video
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Diagnostic and prognostic evaluation of an echocardiography-based artificial intelligence algorithm for detecting HFpEF: a case-control analysis
Assistant Professor A. Chandra (Dallas, US)
Abstract Slides Video

Access the full session

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Machine-learning approaches for the diagnosis of heart failure

The Event

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ESC Congress 2024

30 August 2024 11:00 CET
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