Task decomposition of laparoscopic surgery for objective evaluation of surgical residents' learning curve using hidden Markov model.

Abstract

Evaluation of the laparoscopic surgical skills of surgical residents is usually a subjective process carried out in the operating room by senior surgeons. The two hypotheses of the current study were: (1) haptic information and tool/tissue interactions (types and transitions) performed in laparoscopic surgery are skill-dependent, and (2) statistical models (Hidden Markov Models--HMMs) incorporating these data are capable of objectively evaluating laparoscopic surgical skills. Eight subjects (six residents--two first-year (R1), two third-year (R3), and two fifth-year (R5)--and two expert laparoscopic surgeons) performed laparoscopic cholecystectomy on pigs using an instrumented grasper equipped with force/torque (F/T) sensors at the hand/tool interface, and F/T data was synchronized with video of the operative maneuvers. Fourteen types of tool/tissue (T/T) interactions, each associated with unique F/T signatures, were defined from frame-by-frame video analysis. HMMs for each subject and step of the operation were compared to evaluate the statistical distance between expert surgeons and residents with different skill levels. The statistical distances between HMMs representing expert surgeons and residents were significantly different (alpha < 0.05). Major differences occurred in: (1) F/T magnitudes; (2) type of T/T interactions and transitions between them; and (3) time intervals for each T/T interaction and overall completion time. The greatest difference in performance was between R1 (junior trainee) and R3 (midlevel trainee). Smaller changes were seen as expertise increased beyond the R3 level. HMMs incorporating haptic and visual information provide an objective tool for evaluating surgical skills. Objective evidence for a "learning curve" suggests that surgical residents acquire a major portion of their laparoscopic skill between year 1 and year 3 of training.