Back

Non-invasive estimation of hemodynamic parameters in pulmonary hypertension with a deep learning approach integrating all B-mode cine loops in an echocardiographic exam

29 August 2025 (07:00 - 18:00)
Organised by: Logo
Congress Presentation Part of: Digital tools and wearables: transforming cardiovascular care Deep Learning ESC Professional Premium Access ESC Congress 2025 European Society of Cardiology

About the speaker

Photo
Ms Li-Hsin Cheng

Leiden University Medical Center, Leiden (Netherlands (The))

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
Feasibility of utilizing machine learning to detect coronary artery disease via wearable devices
Mr R. Xu (Chengdu, CN)
Abstract Slides Video
Presentation thumbnail
Semi-supervised deep learning approach for left ventricle segmentation with limited annotations.
Ms F. Aladwani (KW)
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 - 1 September 2025
You may be interested in
thumbnail
Congress Presentation
Stratifying pulmonary hypertension severity in newborns from multi-view echocardiograms using variational autoencoders
thumbnail
Congress Session
Artificial intelligence for today use
thumbnail
Congress Presentation
The added value of echocardiography in PAH risk assessment an AI machine learning-derived analysis of the ULTRA RIGHT VALUE registry

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources