SAGES consensus recommendations on an annotation framework for surgical video.

Journal: Surgical endoscopy
Published Date: Jul 6, 2021

Abstract

BACKGROUND: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration.

Authors

  • Ozanan R Meireles
    Department of Surgery, Massachusetts General Hospital, Boston, MA.
  • Guy Rosman
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA.
  • Maria S Altieri
    Department of Surgery, East Carolina University, Greenville, USA.
  • Lawrence Carin
    Department of Electronic and Computer Engineering, Duke University, Durham, NC, 27705, USA.
  • Gregory Hager
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of engineering, Baltimore, MD, USA.
  • Amin Madani
    Department of Surgery, Columbia University Irving Medical Center, 161 Fort Washington Avenue, New York, NY 10032, USA.
  • Nicolas Padoy
    IHU Strasbourg, Strasbourg, France.
  • Carla M Pugh
  • Patricia Sylla
    Division of Colon and Rectal Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Thomas M Ward
    Surgical AI & Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, 15 Parkman Street, WAC460, Boston, MA 02114, USA.
  • Daniel A Hashimoto
    Department of Surgery, Massachusetts General Hospital, Boston, MA.

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