title: Task versus subtask surgical skill evaluation of robotic minimally invasive surgery.
abstract: 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.
authors.affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. creiley@jhu.edu
authors.forename: Carol E
authors.initials: CE
authors.lastname: Reiley
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Hager
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004521:Educational Measurement / Q000379:methods*; D006801:Humans; D019060:Minimally Invasive Surgical Procedures / Q000193:education*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011361:Professional Competence; D012371:Robotics / Q000193:education*; D025321:Surgery, Computer-Assisted / Q000193:education*; D013647:Task Performance and Analysis; D014481:United States