  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.
