Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.

Journal: European radiology
Published Date: Jun 1, 2021

Abstract

OBJECTIVES: To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis.

Authors

  • Stefanie J Hectors
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Paul Kennedy
    School of Software, University of Technology Sydney, 2007, Sydney, Australia.
  • Kuang-Han Huang
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Daniel Stocker
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Guillermo Carbonell
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Hayit Greenspan
  • Scott Friedman
    Smart Information Flow Technologies (SIFT), Minneapolis.
  • Bachir Taouli
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org.

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