Quality of reporting in AI cardiac MRI segmentation studies – a systematic review and recommendations for future studies

Alabed, S. orcid.org/0000-0002-9960-7587, Maiter, A., Salehi, M. et al. (15 more authors) (2022) Quality of reporting in AI cardiac MRI segmentation studies – a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine, 9. 956811. ISSN 2297-055X

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

© 2022 Alabed, Maiter, Salehi, Mahmood, Daniel, Jenkins, Goodlad, Sharkey, Mamalakis, Rakocevic, Dwivedi, Assadi, Wild, Lu, O’Regan, van der Geest, Garg and Swift. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Keywords: artificial intelligence; machine learning; cardiac MRI; segmentation; systematic review; quality; reporting
Dates:
  • Published: 15 July 2022
  • Published (online): 15 July 2022
  • Accepted: 30 June 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Funding Information:
Funder
Grant number
WELLCOME TRUST (THE)
205188/Z/16/Z
National Institute for Health Research
AI_AWARD01706
DEPARTMENT OF HEALTH AND SOCIAL CARE
AI_AWARD01706
WELLCOME TRUST (THE)
215799/Z/19/Z
Depositing User: Symplectic Sheffield
Date Deposited: 01 Aug 2022 15:05
Last Modified: 01 Aug 2022 15:05
Status: Published
Publisher: Frontiers Media
Refereed: Yes
Identification Number: 10.3389/fcvm.2022.956811
Open Archives Initiative ID (OAI ID):

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