Automated Surgical Skill Assessment in RMIS Training

TL;DR

This paper introduces a novel automated skill assessment method for robotic minimally invasive surgery training, using holistic features from robot kinematic data and a weighted feature fusion technique to improve score prediction accuracy.

Contribution

It proposes a new holistic feature fusion approach for automated surgical skill assessment using robot kinematic data, outperforming previous methods on the JIGSAWS dataset.

Findings

Holistic features outperform HMM-based methods.

Weighted feature fusion improves score prediction.

Achieved up to 0.61 Spearman correlation.

Abstract

Purpose: Manual feedback in basic RMIS training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. While VR-based training tasks can generate automated score reports, there is no mechanism of generating automated feedback for surgeons performing basic surgical tasks in RMIS training. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. Methods: We perform our experiments on the publicly available JIGSAWS dataset and evaluate four different types of holistic features from robot kinematic data - Sequential Motion Texture (SMT), Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Approximate Entropy (ApEn). The features are then used for skill classification and exact skill score prediction. Along with using these features individually, we also evaluate the performance using our proposed weighted combination technique. Results: Our results demonstrate that these holistic features outperform all previous HMM based state-of-the-art methods for skill classification on the JIGSAWS dataset. Also, our proposed feature fusion strategy significantly improves performance for skill score predictions achieving up to 0.61 average spearman correlation coefficient. Conclusions: Holistic features capturing global information from robot kinematic data can successfully be used for evaluating surgeon skill in basic surgical tasks on the da Vinci robot. Using the framework presented can potentially allow for real time score feedback in RMIS training.