Back

Diagnosis of hypertrophic cardiomyopathy by artificial intelligence using standard transthoracic echocardiography

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

About the speaker

Photo
Doctor Nour Karra

Sheba Medical Center, Tel Hashomer (Israel)

7 More presentations in this session

Presentation thumbnail
Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data
Doctor E. Sieliwonczyk (Edegem, BE)
Abstract Slides Video
Presentation thumbnail
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
Presentation thumbnail
Machine learning methods for diagnostic disease prediction and treatment decisions in parvovirus B19 positive viral cardiomyopathy
Doctor C. Baumeier (Berlin, DE)
Abstract Slides Video
Presentation thumbnail
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
Presentation thumbnail
Artificial intelligence to detect pulmonary congestion on chest computed tomography in consecutive dyspnoeic patients
Professor O. Nielsen (Copenhagen, DK)
Abstract Slides Video

Access the full session

Session thumbnail
Machine-learning approaches for the diagnosis of heart failure

The Event

Event poster

ESC Congress 2024

30 August 2024 11:00 CET
You may be interested in
thumbnail
Congress Presentation
Multicenter study on the diagnostic accuracy of left ventricular hypertrophy using a fully automated echocardiographic analysis system with artificial intelligence
thumbnail
Congress Session
Imaging to unravel left ventricular hypertrophy
thumbnail
Congress Presentation
Automated deep learning approach for identifying etiologies of hypertrophic phenocopies by echocardiography

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources