Back

Feasibility of utilizing machine learning to detect coronary artery disease via wearable devices

29 August 2025 (14:15 - 15:00)
Organised by: Logo
Congress Presentation Part of: Digital tools and wearables: transforming cardiovascular care Wearables ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

Photo
Mr Runhao Xu

Chengdu (China)

6 More presentations in this session

Presentation thumbnail
Unveiling the hidden local hemodynamics of peripheral circulation: integration of computational fluid dynamics and diastolic pressure decay
Miss Y. Alan (Istanbul, TR)
Abstract Slides Video
Presentation thumbnail
Implementation of an artificial intelligence-enabled stethoscope in primary care: utilisation and incentive characterisation in the TRICORDER programme
Doctor S. Nakhare (London, GB)
Abstract Slides Video
Presentation thumbnail
Adequately Beta-blocked? Insights from longitudinal wearable data in the All of Us Research Program
Doctor A. Nargesi (Boston, US)
Abstract Slides Video
Presentation thumbnail
Semi-supervised deep learning approach for left ventricle segmentation with limited annotations.
Ms F. Aladwani (KW)
Abstract Slides Video
Presentation thumbnail
Non-invasive estimation of hemodynamic parameters in pulmonary hypertension with a deep learning approach integrating all B-mode cine loops in an echocardiographic exam
Ms L. Cheng (Leiden, NL)
Abstract Slides Video

Access the full session

Session thumbnail
Digital tools and wearables: transforming cardiovascular care

The Event

Event poster

ESC Congress 2025

29 August 2025 14:15 CET
You may be interested in
thumbnail
Congress Presentation
Electrocardiogram-based artificial intelligence to identify prevalent coronary artery disease
thumbnail
Congress Presentation
Risk stratification of coronary artery disease using the artificial intelligence-enabled electrocardiogram
thumbnail
Congress Presentation
Diagnosis and prognosis of coronary artery disease using artificial intelligence and nuclear cardiology: A systematic review

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources