Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
PMID: 38546527

Abstract

OBJECTIVE: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification.

Authors

  • Ishai Levin
    Department of Gynecology, Lis Maternity Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Judith Rapoport Ferman
    Theator Inc, Palo Alto, California, USA.
  • Omri Bar
    FDNA Inc., Boston, MA, USA.
  • Danielle Ben Ayoun
    Theator Inc, Palo Alto, California, USA.
  • Aviad Cohen
    Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel. Electronic address: aviadd@post.bgu.ac.il.
  • Tamir Wolf
    Theator Inc., Palo Alto, CA.

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