A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET–CT images: Initial analysis

Duff, L orcid.org/0000-0002-4295-6356, Scarsbrook, AF orcid.org/0000-0002-4243-032X, Mackie, SL et al. (4 more authors) (2022) A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET–CT images: Initial analysis. Journal of Nuclear Cardiology. ISSN 1071-3581

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022, The Author(s). This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0)
Keywords: Large-vessel vasculitis; FDG PET/CT; Radiomic feature analysis; Diagnosis; Giant cell arteritis
Dates:
  • Accepted: 5 January 2022
  • Published (online): 23 March 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Discovery & Translational Science Dept (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Biomed & Clin Sciences (LIBACS) (Leeds)
Funding Information:
FunderGrant number
British Heart FoundationFS/18/12/33270
Academy of Medical SciencesNot Known
MRC (Medical Research Council)MR/N011775/1
Depositing User: Symplectic Publications
Date Deposited: 24 Feb 2022 14:57
Last Modified: 13 Apr 2022 14:16
Status: Published online
Publisher: Springer
Identification Number: https://doi.org/10.1007/s12350-022-02927-4

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