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Updated: Feb 20, 2026
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
Published on: August 9, 2024
Automated robot-assisted surgical skill evaluation: Predictive analytics approach
Mahtab J Fard1, Sattar Ameri1, R Darin Ellis1
1Department of Industrial and Systems Engineering, Wayne State University, Detroit, Michigan, USA.
Background:
Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.
Methods:
Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied.
Results:
The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.
Conclusion:
This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.
