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Related Experiment Video

Updated: Jul 28, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

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Using hand pose estimation to automate open surgery training feedback.

Eddie Bkheet1, Anne-Lise D'Angelo2, Adam Goldbraikh3

  • 1Data and Decision Sciences, Technion Institute of Technology, Haifa, Israel. eddie.bkheet@gmail.com.

International Journal of Computer Assisted Radiology and Surgery
|May 30, 2023
PubMed
Summary
This summary is machine-generated.

Computer vision estimates 2D hand poses for automated surgical training and footage analysis. This markerless approach achieves high accuracy in gesture segmentation and provides actionable feedback for surgical skill development.

Keywords:
Computer visionGesture recognitionMachine learningPose estimationSurgical skill assessmentSurgical training

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Area of Science:

  • Medical technology
  • Computer vision
  • Surgical education

Background:

  • Automated analysis of surgical footage can enhance surgeon training.
  • 2D hand pose estimation offers a potential markerless solution for analyzing surgical movements.

Purpose of the Study:

  • To utilize computer vision for automated surgical training and footage analysis.
  • To model hand movements and instrument interaction for surgical training benefits.

Main Methods:

  • Developed an in-house dataset of 100 open surgery simulation videos with 2D hand poses.
  • Assessed pose estimation for video segmentation and compared it with kinematic sensors and I3D features.
  • Introduced 6 novel surgical dexterity proxies automatically detectable from video footage.

Main Results:

  • Achieved 88.35% gesture segmentation accuracy by fusing 2D poses and I3D features.
  • Surgical skill proxies showed significant differences between novices and experts.
  • Provided actionable feedback for skill improvement based on pose estimation.

Conclusions:

  • Pose estimation is beneficial for open surgery, offering comparable results to physical sensors for gesture segmentation.
  • Markerless, remote gesture segmentation and skill assessment are feasible.
  • Automated feedback for surgical training can be achieved using pose-estimation-based dexterity proxies.