A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Swift, A.J. orcid.org/0000-0002-8772-409X, Lu, H. orcid.org/0000-0002-0349-2181, Uthoff, J. et al. (11 more authors) (2020) A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis. European Heart Journal - Cardiovascular Imaging. jeaa001. ISSN 2047-2404

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited
Keywords: machine learning; tensor; pulmonary arterial hypertension; diagnosis; right ventricle
Dates:
  • Accepted: 3 January 2020
  • Published (online): 30 January 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
The University of Sheffield > Sheffield Teaching Hospitals
Funding Information:
FunderGrant number
WELLCOME TRUST (THE)205188/Z/16/Z
WELLCOME TRUST (THE)215799/Z/19/Z
BRITISH HEART FOUNDATION33808
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/R014507/1
Depositing User: Symplectic Sheffield
Date Deposited: 06 Mar 2020 15:09
Last Modified: 06 Mar 2020 15:09
Status: Published online
Publisher: Oxford University Press (OUP)
Refereed: Yes
Identification Number: https://doi.org/10.1093/ehjci/jeaa001
Related URLs:

Download

Share / Export

Statistics