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 orcid.org/0000-0003-2483-5873 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, 29 (6). pp. 3315-3331. ISSN 1071-3581

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Item Type: Article
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© 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Large-vessel vasculitis; FDG PET/CT; Radiomic feature analysis; Diagnosis; Giant cell arteritis
Dates:
  • Published: December 2022
  • Published (online): 23 March 2022
  • Accepted: 5 January 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)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Molecular & Personalised Medicine
Funding Information:
Funder
Grant number
British Heart Foundation
FS/18/12/33270
Academy of Medical Sciences
Not Known
MRC (Medical Research Council)
MR/N011775/1
Depositing User: Symplectic Publications
Date Deposited: 24 Feb 2022 14:57
Last Modified: 02 Apr 2023 07:23
Status: Published
Publisher: Springer
Identification Number: 10.1007/s12350-022-02927-4
Open Archives Initiative ID (OAI ID):

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