Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

TL;DR

This paper presents a machine learning framework that objectively classifies surgeon skill levels in robotic-assisted surgery using movement features, achieving high accuracy and offering easy integration into training systems.

Contribution

It introduces a simple, generalizable machine learning approach for automatic skill assessment in RMIS, utilizing movement features to distinguish experts from novices.

Findings

Achieved 85.7% classification accuracy.

Successfully distinguished expert and novice surgeons.

Demonstrated applicability to real surgical data.

Abstract

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.