Comparing machine learning models with established risk scores in predicting bleeding and ischaemic stroke in patients with atrial fibrillation undergoing transcatheter aortic valve implantation

Back

ESC2025 Premium Access

Comparing machine learning models with established risk scores in predicting bleeding and ischaemic stroke in patients with atrial fibrillation undergoing transcatheter aortic valve implantation

Session: Risk stratification models: leveraging digital health for better outcomes

Topic: Machine Learning

Speaker: Professor G. Dangas (New York, US)

Event: ESC Congress 2025

Congress Presentation

Add to my library

About the speaker

Professor George Dangas

Mount Sinai Medical Center, New York (United States of America)

18 presentations

0 follower

7 more presentations in this session

Personalized approach to cardiovascular risk stratification: a randomized controlled trial evaluating the effectiveness of an AI-developed diet versus a standard mediterranean diet

Speaker: Mr B. Rozibaev (Tashkent, UZ)

Prediction of atrial fibrillation from the electrocardiogram in the community using deep learning

Speaker: Doctor L. Brant (Belo Horizonte, BR)

Leveraging AI and clinical guidelines to identify undiagnosed and under-treated CKD patients for treatment optimisation

Speaker: Ms A. Veldman (The Hague, NL)

Quick and easy multi-pathology cardiovascular disease detection with an artificial intelligence-enabled stethoscope: multi-centre prospective validation study in secondary care

Speaker: Doctor P. Bachtiger (London, GB)

Efficiency of an artificial intelligence powered electrocardiogram model for early detection of occlusion myocardial infarction in acute coronary syndrome: a multicenter cohort study

Speaker: Doctor C. Militello (Rome, IT)

Access the full session

Risk stratification models: leveraging digital health for better outcomes

Speakers: Professor G. Dangas, Mr B. Rozibaev, Doctor L. Brant, Ms A. Veldman, Doctor P. Bachtiger...

Access

About the event

ESC Congress 2025

29 August - 1 September 2025

Sessions Presentations

Congress

ESC 365 is supported by

Explore sponsored resources

Explore sponsored resources

Explore sponsored resources