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Area of Science:

  • Neurosurgery
  • Medical Simulation
  • Biomedical Engineering

Background:

  • Surgical gentleness is a key skill, difficult to objectively measure during training.
  • Virtual reality (VR) offers a controlled environment for surgical skill acquisition.
  • Real-time feedback on surgical gentleness can enhance psychomotor and cognitive learning.

Purpose of the Study:

  • To evaluate the use of functional near-infrared spectroscopy (fNIRS) for classifying surgical gentleness in VR training.
  • To identify hemodynamic features indicative of different performance levels.
  • To assess the efficacy of machine learning models in automated performance evaluation.

Main Methods:

  • Twenty-three novices performed a VR laparoscopic task while fNIRS recorded brain activity.
  • Hemodynamic features (slope, RMS, std dev) were extracted from fNIRS signals.
  • Machine learning classifiers were trained to differentiate between low and high gentleness performance scores.

Main Results:

  • Lower gentleness performance correlated with increased right-frontal oxygenated hemoglobin (HbO) and left-motor deoxygenated hemoglobin (HbR) activity.
  • Slope-based fNIRS features outperformed other metrics for classification.
  • Random Forest models using HbR slope achieved high accuracy (≈0.85) and AUC (up to 0.93).

Conclusions:

  • fNIRS-derived hemodynamic metrics show promise for objective, real-time assessment of surgical gentleness in VR.
  • This technology can provide valuable feedback for surgical trainees.
  • Automated assessment using fNIRS can improve the efficiency and effectiveness of VR surgical training.