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Objective Pupillometry and Dexterity Assessment in Surgical Trainees: A Pilot Study.

Patrick Kramer1, Kelly Jiang1, Carly Weber-Levine1

  • 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Journal of Surgical Education
|April 15, 2026
PubMed
Summary

This study combined dexterity tests with pupillometry to assess surgical skill and cognitive load. High-performing surgeons showed lower cognitive load, suggesting greater task automation.

Keywords:
Cognitive loadDexterity testingPupillometrySurgical educationTechnical skill acquisition

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Area of Science:

  • Surgical Education and Training
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Surgical skill acquisition relies on motor coordination and cognitive control.
  • Existing assessment tools have variable predictive validity.
  • Physiologic metrics like pupillometry offer objective measures of cognitive load.

Purpose of the Study:

  • To develop a unified framework integrating dexterity metrics and pupillometry for surgical training assessment.
  • To evaluate technical performance and cognitive load across different surgical training levels.
  • To explore the relationship between objective performance and cognitive workload.

Main Methods:

  • A pilot study involving 79 participants (medical students, controls, experts).
  • Standardized dexterity tasks (Finger Dexterity, Tweezer Dexterity, knot-tying) and performance metrics (completion time).
  • Cognitive load assessment using baseline-corrected pupil diameter (Δ-BCPD) synchronized with task time.

Main Results:

  • Finger dexterity scores varied significantly by training level and correlated with knot-tying time.
  • Pupillometry indicated lower cognitive load (Δ-BCPD) in experts and high-performing participants.
  • High performers exhibited reduced cognitive load, suggesting task automation, particularly in fine motor tasks.

Conclusions:

  • Dexterity tasks combined with pupillometry offer complementary insights into surgical performance and workload.
  • This rater-independent framework shows potential for differentiating performance and characterizing task demands.
  • Further validation in larger cohorts is recommended for this promising assessment approach.