Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.

Journal: Surgical endoscopy
PMID: 33398560

Abstract

BACKGROUND: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures.

Authors

  • Roi Anteby
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. roianteby@mail.tau.ac.il.
  • Nir Horesh
    Faculty of Medicine, Tel Aviv University, Tel Aviv, 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.).
  • Yaniv Zager
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Imri Amiel
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Danny Rosin
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Mordechai Gutman
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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