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Open surgery tool classification and hand utilization using a multi-camera system.

Kristina Basiev1, Adam Goldbraikh2, Carla M Pugh3

  • 1Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, 3200003, Haifa, Israel. kris.basiev@gmail.com.

International Journal of Computer Assisted Radiology and Surgery
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-camera system for classifying surgical tools and identifying hand-held instruments, achieving 93% accuracy. The approach effectively integrates multiple views to overcome occlusions and track tools even when not in frame.

Keywords:
Artificial intelligenceMulti-cameraObject detectionOpen surgerySurgical toolsSurgical video data

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Area of Science:

  • Computer Vision
  • Surgical Technology
  • Medical Robotics

Background:

  • Multi-camera systems in surgery can prevent occlusions and provide comprehensive views.
  • Fusing data from multiple cameras presents challenges, such as tools being visible in one view but not another.

Purpose of the Study:

  • To classify open surgery tools and identify which tool is held in each hand using multi-camera video.
  • To develop a system that defines a 'global ground truth' for tool presence, regardless of visibility in any single camera.

Main Methods:

  • Utilized a simulated open bowel repair with 48 participants.
  • Employed top-view and close-up cameras, with YOLOv5 for tool and hand detection.
  • Integrated spatial, temporal, and multi-camera data using high-frequency (30 fps) and low-frequency (3 fps) LSTMs.

Main Results:

  • The final multi-camera classifier achieved an accuracy of 0.93 and an F1 score of 0.94.
  • Individual camera views (top-view: 0.88/0.88, close-up: 0.81/0.83) and combined cameras (0.9/0.9) showed lower performance.
  • High-fps LSTM (0.92/0.93) and low-fps LSTM (0.9/0.91) also demonstrated strong but ultimately surpassed results.

Conclusions:

  • A 'global ground truth' definition, established during data labeling, enables robust tool classification by accounting for tools not currently in view.
  • Combining high and low frame-rate LSTMs from a multi-camera array significantly improved classification of the global ground truth.
  • This approach enhances surgical workflow analysis by accurately tracking tools throughout procedures, even with occlusions.