An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images

Duff, LM orcid.org/0000-0002-4295-6356, Scarsbrook, AF orcid.org/0000-0002-4243-032X, Ravikumar, N orcid.org/0000-0003-0134-107X et al. (10 more authors) (2023) An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules, 13 (2). 343. ISSN 2218-273X

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Item Type: Article
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© 2023 by the authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: aortitis; radiomics; machine learning; convolutional neural network; positron emission tomography/computed tomography
Dates:
  • Published: February 2023
  • Published (online): 9 February 2023
  • Accepted: 1 February 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
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)
Depositing User: Symplectic Publications
Date Deposited: 10 Mar 2023 10:18
Last Modified: 25 Jun 2023 23:16
Status: Published
Publisher: MDPI
Identification Number: 10.3390/biom13020343
Open Archives Initiative ID (OAI ID):

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