Deep learning for noninvasive liver fibrosis classification: A systematic review.

Journal: Liver international : official journal of the International Association for the Study of the Liver
Published Date: Oct 1, 2021

Abstract

BACKGROUND AND AIMS: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging.

Authors

  • Roi Anteby
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. roianteby@mail.tau.ac.il.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nir Horesh
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Ido Nachmany
    Sackler Medical School, Tel Aviv University, Tel Aviv, Israel.
  • Orit Shimon
    From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Uri Kopylov
    Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Shelly Soffer
    From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).

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