Diagnosing osteoarthritis from T maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort.

Journal: Osteoarthritis and cartilage
Published Date: Mar 21, 2019

Abstract

OBJECTIVE: We aim to study to what extent conventional and deep-learning-based T relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA).

Authors

  • V Pedoia
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Center of Digital Health Innovation (CDHI), USA. Electronic address: valentina.pedoia@ucsf.edu.
  • J Lee
  • B Norman
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: berknorman@me.com.
  • T M Link
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA. Electronic address: Thomas.Link@ucsf.edu.
  • S Majumdar
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Center of Digital Health Innovation (CDHI), USA. Electronic address: Sharmila.Majumdar@ucsf.edu.

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