A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

Badesha, A.S., Frood, R. orcid.org/0000-0003-2681-9922, Bailey, M.A. orcid.org/0000-0001-5038-1970 et al. (2 more authors) (2024) A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease. Tomography, 10 (9). pp. 1455-1487. ISSN 2379-1381

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Item Type: Article
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© 2024 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: radiomics; artificial intelligence; machine learning; cardiovascular disease; carotid; coronary; CT angiography; CT coronary angiography; PET; molecular imaging
Dates:
  • Accepted: 30 August 2024
  • Published (online): 3 September 2024
  • Published: September 2024
Institution: The University of Leeds
Academic Units: 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)
Depositing User: Symplectic Publications
Date Deposited: 19 May 2025 13:51
Last Modified: 19 May 2025 13:51
Status: Published
Publisher: MDPI
Identification Number: 10.3390/tomography10090108
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
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