TY - GEN
T1 - Task versus subtask surgical skill evaluation of robotic minimally invasive surgery
AU - Reiley, Carol E.
AU - Hager, Gregory D.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-642-04268-3_54
DO - 10.1007/978-3-642-04268-3_54
M3 - Conference contribution
C2 - 20426017
AN - SCOPUS:81955165236
SN - 3642042678
SN - 9783642042676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 435
EP - 442
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -