Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

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.