Deep learning for surgical phase recognition using endoscopic videos

Annetje C.P. Guédon*, Senna E.P. Meij, Karim N.M.M.H. Osman, Helena A. Kloosterman, Karlijn J. van Stralen, Matthijs C.M. Grimbergen, Quirijn A.J. Eijsbouts, John J. van den Dobbelsteen, Andru P. Twinanda

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
29 Downloads (Pure)


perating room planning is a complex task as pre-operative estimations of procedure duration have a limited accuracy. This is due to large variations in the course of procedures. Therefore, information about the progress of procedures is essential to adapt the daily operating room schedule accordingly. This information should ideally be objective, automatically retrievable and in real-time. Recordings made during endoscopic surgeries are a potential source of progress information. A trained observer is able to recognize the ongoing surgical phase from watching these videos. The introduction of deep learning techniques brought up opportunities to automatically retrieve information from surgical videos. The aim of this study was to apply state-of-the art deep learning techniques on a new set of endoscopic videos to automatically recognize the progress of a procedure, and to assess the feasibility of the approach in terms of performance, scalability and practical considerations.

Original languageEnglish
Pages (from-to)6150-6157
JournalSurgical Endoscopy
Volume35 (2021)
Issue number11
Publication statusPublished - 2020

Bibliographical note

Acccepted Author Manuscript


  • Automatic recognition
  • Deep learning
  • Endoscopic videos
  • Surgical phase


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