  Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video analysis or acceleration data. In this paper, we present a novel
approach for automated assessment of OSATS based surgical skills and provide an
analysis of different features on multi-modal data (video and accelerometer
data). Methods: We conduct the largest study, to the best of our knowledge, for
basic surgical skills assessment on a dataset that contained video and
accelerometer data for suturing and knot-tying tasks. We introduce "entropy
based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
(XApEn), which quantify the amount of predictability and regularity of
fluctuations in time-series data. The proposed features are compared to
existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
Results: We report average performance of different features across all
applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
that the proposed entropy based features out-perform previous state-of-the-art
methods using video data. For accelerometer data, our method performs better
for suturing only. We also show that fusion of video and acceleration features
can improve overall performance with the proposed entropy features achieving
highest accuracy. Conclusions: Automated surgical skills assessment can be
achieved with high accuracy using the proposed entropy features. Such a system
can significantly improve the efficiency of surgical training in medical
schools and teaching hospitals.
