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

Cheng, L., Alabed, S. orcid.org/0000-0002-9960-7587, Charalampopoulos, A. et al. (8 more authors) (2025) Non-invasive estimation of hemodynamic parameters in pulmonary hypertension with a deep learning approach integrating all B-mode cine loops in an echocardiographic exam. In: European Heart Journal. ESC Congress 2025 together with World Congress of Cardiology, 29 Aug - 01 Sep 2025, Madrid, Spain. Vol. 46 (Supplement_1). Oxford University Press (OUP). Article no: ehaf784.4395, pp. 2818-2833. ISSN: 0195-668X. EISSN: 1522-9645.

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Copyright, Publisher and Additional Information:

© The Author(s) 2025.
Keywords: Biomedical and Clinical Sciences; Cardiovascular Medicine and Haematology; Biomedical Imaging; Machine Learning and Artificial Intelligence; Cardiovascular; Networking and Information Technology R&D (NITRD); Clinical Research; Bioengineering; Lung; Heart Disease; Pulmonary Hypertension; Cardiovascular
Dates:
  • Published (online): 5 November 2025
  • Published: November 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection, Immunity and Cardiovascular Disease
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Funding Information:
Funder
Grant number
BRITISH HEART FOUNDATION
FS/SCRF/24/32034
Date Deposited: 04 Sep 2026 09:36
Last Modified: 04 Sep 2026 09:36
Status: Published
Publisher: Oxford University Press (OUP)
Refereed: Yes
Identification Number: 10.1093/eurheartj/ehaf784.4395
Related URLs:
Open Archives Initiative ID (OAI ID):

Export

Statistics

View Altmetric information about this item.


CORE (COnnecting REpositories)