Markov Modeling in Minimally Invasive Surgery… in

A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally preformed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process is preformed using fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Discrete Hidden Markov Models (DHMM). Ten surgeons (5 Novice Surgeons- NS; 5 Expert Surgeons- ES) performed a cholecystectomy and Nissen fundoplication in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque sensor was used to measure the forces/torqu s at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, defined force/torque signatures for 14 types of tool/tissue interactions. From each step of the surgical procedures, two DHMM were developed representing the performance of 3 surgeons randomly selected from the 5 in the ES and NS groups. The data obtained by the remaining 2 surgeons in each group were used for evaluating the performance scale. Th