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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition.

Sarah Choksi1, Skyler Szot2, Chengbo Zang2

  • 1Intraoperative 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.

Surgical Endoscopy
|August 14, 2023
PubMed
Summary

This study developed an AI-powered system for real-time surgical phase recognition in robotic inguinal hernia repairs. The computer vision model achieved 68.7% average accuracy, aiding workflow optimization and surgical assessment.

Keywords:
Artificial intelligence in surgeryComputer visionInguinal hernia repairMachine learningReal time computer vision

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Area of Science:

  • Robotic surgery
  • Computer vision
  • Artificial intelligence

Background:

  • Automating surgical phase recognition is crucial for advancing computer vision (CV) algorithms in surgery.
  • CV, a form of AI, utilizes deep learning (DL) for image interpretation, enhanced by GPU computing for real-time video analysis.
  • Edge computing enables real-time CV algorithm application, essential for optimizing surgical workflows.

Purpose of the Study:

  • To implement and evaluate a real-time phase recognition workflow for robotic inguinal hernia repairs (RIHR).
  • To assess the performance of a DL-based CV model for automated surgical phase prediction during procedures.

Main Methods:

  • A DL model with a ResNet-50 backbone was trained on 211 RIHR videos annotated into 14 phases.
  • The model was deployed on an Nvidia Jetson Xavier NX edge computing device for real-time processing.
  • Post-recording processing, including phase merging into seven phases and frame averaging, refined predictions.

Main Results:

  • The real-time phase recognition model achieved an average accuracy of 68.7% on 10 RIHR procedures.
  • Predictions were generated at approximately 250 ms latency, once per second.
  • The model demonstrated a prediction accuracy range of 59.8% to 78.2%.

Conclusions:

  • A real-time CV-based phase prediction system for RIHR was successfully implemented.
  • This system can enhance surgical progress monitoring.
  • Integration into software can optimize hospital workflows.