Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis.

Journal: Gastrointestinal endoscopy
Published Date: Apr 22, 2020

Abstract

BACKGROUND AND AIMS: Deep learning is an innovative algorithm based on neural networks. Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small-bowel diseases. Manual examination of WCE is time-consuming and can benefit from automatic detection using artificial intelligence (AI). We aimed to perform a systematic review of the current literature pertaining to deep learning implementation in WCE.

Authors

  • 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.).
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • 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.).
  • Noy Nachmias
    Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; Departments of Internal Medicine D, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Rami Eliakim
    Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Shomron Ben-Horin
    Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Uri Kopylov
    Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.

Keywords

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