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Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.

Francisco Luongo1, Ryan Hakim2, Jessica H Nguyen2

  • 1Department of Biology and Biological Engineering, Caltech, Pasadena, CA.

Surgery
|September 29, 2020
PubMed
Summary
This summary is machine-generated.

This study developed deep learning computer vision models to automatically identify and classify suturing gestures during robotic radical prostatectomy. The models achieved high accuracy, demonstrating potential for automated surgical skill assessment.

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Area of Science:

  • Robotics
  • Computer Vision
  • Surgical Skill Assessment

Background:

  • Previous work established a taxonomy of suturing gestures in robotic radical prostatectomy.
  • Suturing gestures are linked to tissue tears and patient outcomes.

Purpose of the Study:

  • To train deep learning computer vision models for automated identification and classification of suturing gestures.
  • To assess the performance of these models in recognizing needle driving attempts.

Main Methods:

  • Manual annotation of live suturing video clips by two independent raters.
  • Compilation of identification (2,395 videos) and classification (511 videos) datasets.
  • Training computer vision models using raw pixel data and optical flow, with 80/20 train/test splits.

Main Results:

  • Models reliably predicted gesture presence (Area Under Curve: 0.88) and type (Area Under Curve: 0.87) above chance levels.
  • No significant difference in performance between recurrent classification model choices (LSTM vs. ConvLSTM).

Conclusions:

  • Computer vision can accurately identify and classify suturing gestures.
  • Deep learning holds potential for automating surgical skill assessment in procedures like robotic radical prostatectomy.