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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Task versus Subtask Surgical Skill Evaluation of Robotic Minimally Invasive Surgery
verfasst von : Carol E. Reiley, Gregory D. Hager
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
Verlag: Springer Berlin Heidelberg
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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.