In this paper, we provide a clinical motivation for the importance of surgical skill evaluation. We review the current methods of tracking surgical motion and the available data-collection systems. We also survey current methods of surgical skill evaluation and show that most approaches fall into one of three methods: (1) structured human grading; (2) descriptive statistics; or (3) statistical language models of surgical motion. We discuss the need for an encompassing approach to model human skill through statistical models to allow for objective skill evaluation.
Visual force feedback resulted in reduced suture breakage, lower forces, and decreased force inconsistencies among novice robotic surgeons, although elapsed time and knot quality were unaffected. In contrast, visual force feedback did not affect these metrics among surgeons experienced with the da Vinci system. These results suggest that visual force feedback primarily benefits novice robot-assisted surgeons, with diminishing benefits among experienced surgeons.
Evaluating surgical skill is a time consuming, subjective, and difficult process. This paper compares two methods of identifying the skill level of a subject given motion data from a benchtop surgical task. In the first method, we build discrete Hidden Markov Models at the task level, and test against these models. In the second method, we build discrete Hidden Markov Models of surgical gestures, called surgemes, and evaluate skill at this level. We apply these techniques to 57 data sets collected from the da Vinci surgical system. Our current techniques have achieved accuracy levels of 100% using task level models and known gesture segmentation, 95% with task level models and unknown gesture segmentation, and 100% with the surgeme level models in correctly identifying the skill level. We observe that, although less accurate, the second method requires less prior label information. Also, the surgeme level classification provided more insights into what subjects did well, and what they did poorly.
Abstract. This paper addresses automatic skill assessment in robotic minimally invasive surgery. Hidden Markov models (HMMs) are developed for individual surgical gestures (or surgemes) that comprise a typical bench-top surgical training task. It is known that such HMMs can be used to recognize and segment surgemes in previously unseen trials [1]. Here, the topology of each surgeme HMM is designed in a data-driven manner, mixing trials from multiple surgeons with varying skill levels, resulting in HMM states that model skill-specific sub-gestures. The sequence of HMM states visited while performing a surgeme are therefore indicative of the surgeon's skill level. This expectation is confirmed by the average edit distance between the state-level "transcripts" of the same surgeme performed by two surgeons with different expertise levels. Some surgemes are further shown to be more indicative of skill than others.
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