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Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment.

Ramandeep Singh¹, Anoop Kant Godiyal², Parikshith Chavakula¹

¹Neuro-Engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110029, India.

Bioengineering (Basel, Switzerland)

|April 28, 2023

Summary

This study developed a 3D printed craniotomy simulator with realistic haptic feedback for neurosurgery training. Force myography and machine learning objectively assess surgical skills, achieving 90% accuracy in classifying expertise levels.

Keywords:

3D printing artificial intelligence bone matrix drilling force myography surgical skills

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Area of Science:

Neurosurgery
Medical Simulation
Biomedical Engineering

Background:

Craniotomy simulation is crucial for neurosurgical skill development.
Traditional skill assessment is subjective and time-consuming.
Objective evaluation methods are needed for surgical training.

Purpose of the Study:

To develop an anatomically accurate craniotomy simulator with realistic haptic feedback.
To implement objective surgical skill evaluation using force myography (FMG) and machine learning.
To assess the effectiveness of the simulator and FMG-based assessment.

Main Methods:

Developed a CT scan segmentation-based craniotomy simulator using 3D printed bone matrix material.
Utilized force myography (FMG) sensors and machine learning classifiers (Naïve Bayes, LDA, SVM, DT) for skill assessment.
Recruited 22 neurosurgeons (novice, intermediate, expert) for drilling experiments and feedback collection.

Main Results:

The simulator provided effective haptic feedback (average score 7.1) and was rated as a valuable training tool.
Naïve Bayes classifier achieved the highest accuracy (90.0 ± 14.8%) in classifying surgical expertise.
Other classifiers showed varying accuracies: DT (86.22 ± 20.8%), LDA (81.9 ± 23.6%), SVM (76.7 ± 32.9%).

Conclusions:

3D printed materials with biomechanical properties similar to real tissues enhance surgical simulation effectiveness.
Force myography combined with machine learning offers an objective and automated method for assessing surgical drilling skills.
The developed simulator and FMG-based assessment system show promise for improving neurosurgical training.