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Updated: Sep 28, 2025

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Eye Tracking and Motion Data Predict Endoscopic Sinus Surgery Skill.

Alexandra J Berges1, S Swaroop Vedula2, Alejandro Chara1

  • 1School of Medicine, Johns Hopkins University School of Medicine, Baltimore, USA.

The Laryngoscope
|March 31, 2022
PubMed
Summary
This summary is machine-generated.

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Objective metrics from eye tracking and instrument motion can accurately assess endoscopic sinus surgery (ESS) skill. This technology offers automated feedback for surgical trainees, improving learning curves in endoscopic procedures.

Area of Science:

  • Surgical Education and Training
  • Medical Technology and Innovation
  • Otolaryngology and Head and Neck Surgery

Background:

  • Endoscopic surgery presents a steep learning curve due to altered visual-motor coordination and reduced sensory feedback.
  • Endoscopic sinus surgery (ESS) specifically lacks objective metrics for evaluating trainee proficiency and providing targeted feedback.

Purpose of the Study:

  • To identify key summary metrics from eye tracking, endoscope motion, and instrument motion.
  • To objectively assess and differentiate surgical skill levels in endoscopic sinus surgery (ESS).
  • To develop a data-driven approach for skill assessment in surgical training.

Main Methods:

  • Expert and novice surgeons performed standardized ESS tasks on cadavers.
  • Eye gaze data were captured using infrared cameras, while endoscope and instrument motion were tracked via electromagnetic sensors.
Keywords:
endoscopic sinus surgeryeye movement eventseye trackingsurgical skill assessmentsurgical training

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  • 20 summary statistics were computed and analyzed using logistic regression to predict skill levels based on expert assessments.

    • Main Results:

    • Fourteen motion and eye gaze metrics significantly distinguished between high- and low-skill surgeons.
    • Predictive models achieved high accuracy (AUC > 0.95) for most ESS tasks.
    • A combined model demonstrated strong predictive power (AUC 0.95) using transferable metrics from eye tracking and endoscope motion.

    Conclusions:

    • Objective data from eye gaze, endoscope, and instrument motion accurately reflect ESS surgical performance.
    • Algorithmic analysis of these parameters can enable automated, objective skill assessment for surgical trainees.
    • This approach holds potential for enhancing the learning curve in endoscopic surgery.