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Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm.

Somayeh B Shafiei¹, Saeed Shadpour², James L Mohler¹

¹Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY.

Annals of Surgery Open : Perspectives of Surgical History, Education, and Clinical Approaches

|June 12, 2023

Summary

This summary is machine-generated.

Visual metrics from eye gaze data accurately classify surgical expertise in robot-assisted surgery (RAS). Machine learning models can assess skill levels and Global Evaluative Assessment of Robotic Skills (GEARS) measures, highlighting the importance of visual cues over task completion time.

Keywords:

Robot-assisted surgery expertise level visual metrics

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Area of Science:

Robotics
Surgical Education
Machine Learning

Background:

Assessing surgical skills is vital for enhancing training and ensuring patient care quality.
Robot-assisted surgery (RAS) requires objective methods for evaluating surgeon expertise.
Visual metrics offer a promising avenue for skill assessment in complex surgical procedures.

Purpose of the Study:

To develop a gradient boosting classification model (GBM) for classifying surgical expertise in RAS.
To utilize visual metrics derived from eye gaze data for skill level classification.
To evaluate the association between visual metrics and established surgical skill assessment tools (GEARS).

Main Methods:

Recorded eye gaze data from 11 participants performing four distinct RAS subtasks.
Extracted visual metrics from eye gaze data to represent surgical performance.
Employed a gradient boosting classification model (GBM) and ANOVA for skill classification and analysis.

Main Results:

Achieved high classification accuracies (95-96%) for various surgical subtasks using visual metrics.
Demonstrated significant differences in performance across skill levels for all subtasks (p<0.01).
Found strong associations between extracted visual metrics and Global Evaluative Assessment of Robotic Skills (GEARS) metrics (R² > 0.7).

Conclusions:

Machine learning models trained on visual metrics effectively classify surgical skill levels in RAS.
Visual metrics can be used to evaluate Global Evaluative Assessment of Robotic Skills (GEARS) measures.
Task completion time alone is insufficient for comprehensive surgical skill assessment.