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Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement

Mallory J Raymond1,2, Biswajit Biswal3, Royal M Pipaliya1,4

  • 1Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Jacksonville, USA.

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Summary
This summary is machine-generated.

A new computer vision model accurately tracks surgical instruments during mastoidectomies. This technology can objectively analyze surgeon technique, improving training and patient safety in cochlear implantation procedures.

Keywords:
computer visioninstrument recognitionmastoidectomy

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Area of Science:

  • Neurosurgery
  • Computer Vision
  • Surgical Technology

Background:

  • Mastoidectomy is a critical surgical procedure, often for cochlear implantation.
  • Objective assessment of surgical skill in mastoidectomy is challenging.
  • Current methods for evaluating surgical performance are often subjective.

Purpose of the Study:

  • To develop a convolutional neural network (CNN)-based computer vision model.
  • To recognize and track the drill and suction-irrigator during mastoidectomies.
  • To enable objective analysis of surgical technique.

Main Methods:

  • Utilized intraoperative video recordings of mastoidectomies.
  • Developed a tracking module using a feature pyramid network and DETECTRON.
  • Trained the model on 8 videos and tested on 2 videos, measuring Intersection over Union (IoU), accuracy, and average precision.

Main Results:

  • Achieved high mean average precision: 99% for the drill and 86% for the suction-irrigator at an IoU of 0.5.
  • The model successfully generated directional and distance maps for instrument movements.
  • Demonstrated excellent precision in identifying and tracking surgical instruments.

Conclusions:

  • The developed computer vision model precisely tracks surgical instruments in mastoidectomy videos.
  • This tool can be used to retrospectively analyze objective surgical measures (e.g., motion economy, speed, coordination).
  • Facilitates the characterization of objective benchmarks for safe and efficient mastoidectomies, enhancing surgical training and outcomes.