Back

The association between cardiovascular health and physiologic age as determined by artificial intelligence-enabled electrocardiography

31 August 2024 (10:00 - 10:50)
Organised by: Logo
Congress Presentation Part of: Machine learning in preventive cardiology Cardiovascular Risk Assessment ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

Photo
Doctor Laura Melissa Ortega Aviles

Rochester (United States of America)

8 More presentations in this session

Presentation thumbnail
Machine learning identifies individuals at higher risk of incident cardio-renal-metabolic diseases and cardiovascular death who have unrealised opportunities to reduce future cardiovascular risk
Doctor M. Haris (Leeds, GB)
Abstract Slides Video
Presentation thumbnail
The relationship between retinal vascular parameters measured by artificial intelligence and the risk of new-onset cardiovascular disease in a Chinese community-based population
Doctor F. Fan (Beijing, CN)
Abstract Slides Video
Presentation thumbnail
Predicting atrial fibrillation within 7 years follow-up using machine learning models
Miss K. Tsarapatsani (Ioannina, GR)
Abstract Slides Video
Presentation thumbnail
Coronary artery calcium scans powered by artificial intelligence (AI-CAC) predicts atrial fibrillation and stroke comparably to cardiac magnetic resonance imaging: MESA
Doctor M. Naghavi (Houston, US)
Abstract Slides Video
Presentation thumbnail
Opportunistic screening of coronary artery calcification on non-gated conventional CT scans using artificial intelligence
Doctor Y. Aviv (Petah Tikva, IL)
Abstract Slides Video

Access the full session

Session thumbnail
Machine learning in preventive cardiology

The Event

Event poster

ESC Congress 2024

31 August 2024 10:00 CET
You may be interested in
thumbnail
Congress Presentation
Electrocardiographic age as a predictor of cardiovascular events in Brazilian adults: from the ELSA-Brasil study
thumbnail
Congress Presentation
Prognostic assessment based on the trajectory of artificial intelligence-enabled electrocardiograms for age estimation
thumbnail
Congress Presentation
AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources