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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Varadarajan, B., Reiley, C., Lin, H., Khudanpur, S., Hager, G. (2009). Data-Derived Models for Segmentation with Application to Surgical Assessment and Training. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_53
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DOI: https://doi.org/10.1007/978-3-642-04268-3_53
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