Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition.

Journal: Surgical endoscopy
PMID: 37580578

Abstract

BACKGROUND: Automation of surgical phase recognition is a key effort toward the development of Computer Vision (CV) algorithms, for workflow optimization and video-based assessment. CV is a form of Artificial Intelligence (AI) that allows interpretation of images through a deep learning (DL)-based algorithm. The improvements in Graphic Processing Unit (GPU) computing devices allow researchers to apply these algorithms for recognition of content in videos in real-time. Edge computing, where data is collected, analyzed, and acted upon in close proximity to the collection source, is essential meet the demands of workflow optimization by providing real-time algorithm application. We implemented a real-time phase recognition workflow and demonstrated its performance on 10 Robotic Inguinal Hernia Repairs (RIHR) to obtain phase predictions during the procedure.

Authors

  • Sarah Choksi
    Intraoperative Performance Analytics Laboratory (IPAL), Department of Surgery, Lenox Hill Hospital, Northwell Health, 186 E 76th Street, 1st Fl, New York, NY, 10021, USA. Schoksi1@northwell.edu.
  • Skyler Szot
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Chengbo Zang
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Kaan Yarali
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Yuqing Cao
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Feroz Ahmad
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Zixuan Xiang
    Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA.
  • Daniel P Bitner
    Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA. DBitner@northwell.edu.
  • Zoran Kostic
    Columbia University, Fu Foundation School of Engineering and Applied Science, Department of Electrical Engineering, New York, NY.
  • Filippo Filicori
    Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, 186 E. 76th Street, 1st Floor, New York, NY, 10021, USA.

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