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Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning.

Pierre F Baldi1,2,3,4, Sherif Abdelkarim1,2, Junze Liu1,2

  • 1Department of Computer Science, University of California, Irvine, CA, USA.

Translational Vision Science & Technology
|January 17, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can analyze surgical videos to track instruments in 3D space, identifying type, depth, and insertion side. This deep learning approach shows potential for real-time surgical feedback and technique analysis.

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Technology

Background:

  • Surgical instrument tracking in the vitreous cavity is crucial for precision and safety.
  • Current methods for analyzing surgical technique can be labor-intensive and subjective.

Purpose of the Study:

  • To evaluate artificial intelligence (AI)-based video analysis for determining surgical instrument characteristics in 3D vitreous space.
  • To assess the potential of deep learning models for real-time surgical instrument tracking and analysis.

Main Methods:

  • A model eye was created to record videos of surgical instruments moving in 3D space.
  • Videos were frame-labeled to identify tool type, location, depth, and insertion laterality.
  • Two deep learning models were trained and evaluated for predicting instrument characteristics.

Main Results:

  • Classification models achieved high accuracy: 83-84% for x-y location, 96-97% for depth, and 100% for instrument type and laterality.
  • A close-up detection model processed at 67 frames per second with a mean average precision of 79.3%.
  • AI models demonstrated near-instantaneous performance in tracking and characterizing surgical instruments.

Conclusions:

  • Trained AI models can accurately track surgical instruments in 3D space, determining key characteristics.
  • The speed and accuracy of these models support their investigation for real-world surgical video analysis.
  • Deep learning offers potential for safety feedback and surgical technique metric extraction to optimize outcomes.