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Artificial Intelligence Tracking of Otologic Instruments in Mastoidectomy Videos.

George S Liu1, Sharad Parulekar1, Melissa C Lee1

  • 1Department of Otolaryngology-Head and Neck Surgery, Stanford University.

Otology & Neurotology : Official Publication of the American Otological Society, American Neurotology Society [And] European Academy of Otology and Neurotology
|October 30, 2024
PubMed
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This summary is machine-generated.

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An AI model accurately tracks surgical drills in mastoidectomy videos, enabling objective surgical skill analysis. This technology aids in developing future surgical navigation and augmented reality systems.

Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mastoidectomy involves complex instrument manipulation requiring precise control.
  • Objective assessment of surgical skill in otologic procedures is challenging due to reliance on subjective evaluation.
  • Developing automated tools can enhance surgical training and performance feedback.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for tracking otologic instruments during mastoidectomy procedures.
  • To assess the accuracy and speed of the AI model in identifying and tracking surgical tools in video recordings.
  • To explore the potential of AI-driven analysis for objective surgical skill assessment.

Main Methods:

  • A retrospective case series utilizing 13 cadaveric mastoidectomy videos recorded by otolaryngology residents.

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  • The YOLOv8 computer vision model was adapted for semi-manual annotation and tracking of the suction irrigator and drill.
  • Performance was evaluated using precision, recall, and mean average precision (mAP50), with analysis of drill speed in prospective videos.

    • Main Results:

    • The AI model demonstrated high accuracy in tracking the drill (mAP50 = 0.93) and lower accuracy for the suction irrigator (mAP50 = 0.62).
    • Near real-time prediction speed of approximately 100 milliseconds per image was achieved.
    • Analysis of prospective videos indicated significantly higher mean drill speed and duration of high-speed drilling in attending surgeon procedures compared to residents.

    Conclusions:

    • An AI model can effectively track surgical drills in mastoidectomy videos with high precision and speed.
    • Automated instrument tracking facilitates objective surgical skill analysis, eliminating the need for manual annotation.
    • This technology offers valuable data for advancing surgical navigation and augmented reality applications in otologic surgery.