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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
Published on: August 9, 2024
822
Using AI and computer vision to analyze technical proficiency in robotic surgery.
Janice H Yang1,2, Emmett D Goodman1,2, Aaron J Dawes3,4
1Department of Computer Science, Stanford University, Stanford, CA, USA.
Surgical Endoscopy
|December 19, 2022
Summary
This study developed an AI-powered computer vision system to objectively assess surgical skills in robotic colorectal surgery. The AI tool demonstrated a correlation with expert surgeon ratings, showing promise for efficient and standardized surgical training.
Area of Science:
- Robotics
- Artificial Intelligence
- Surgical Education
Background:
- Intraoperative skills assessment is currently subjective and time-consuming.
- There is a need for objective and efficient methods for surgical feedback.
- Computer vision and artificial intelligence (AI) offer potential solutions for automated assessment.
Purpose of the Study:
- To design and validate an interpretable, AI-based automated method for evaluating technical proficiency in robotic colorectal surgery.
- To compare AI-driven skill assessment with expert surgeon evaluations.
Main Methods:
- 92 video clips of peritoneal closure from robotic colorectal surgery were analyzed.
- Expert surgeons (n=6) rated surgical skill using the GEARS assessment tool.
- Deep learning computer vision algorithms were developed for surgical tool detection and tracking.
Main Results:
- AI-determined total tool movement positively correlated with surgeon ratings of technical efficiency (r=-0.72).
- More proficient surgeons exhibited significantly less tool movement (p<0.001).
- AI-calculated bimanual movement correlated with surgeon ratings of bimanual dexterity (r=0.48), with higher-skilled clips showing more simultaneous bimanual movement (p<0.001).
Conclusions:
- AI-derived measurements of technical proficiency correlate with expert surgeon assessments.
- Computer vision AI shows promise for standardizing and improving the efficiency of surgical technique grading.
- This approach can potentially enhance surgical skills training and feedback mechanisms.
Keywords:
Artificial intelligenceColorectal surgeryComputer visionGEARSRobotic surgerySkills assessment
