Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills

The best method of training for laparoscopic surgical skills is controversi al. Some advocate observation in the operating room, while others promote a nimal and simulated models or a combination of surgery-related tasks. A cru cial process in surgical education is to evaluate the level of surgical ski lls. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a stude nt. By its nature, this process uses fuzzy criteria, The objective of the c urrent study was to develop and assess a skill scale using Markov models (M Ms), Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)I pe rformed a cholecystectomy and Nissen fundoplication in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque ( F/T) sensor was used to measure the forces/torques at the hand/tool interfa ce synchronized with a video of the tool operative maneuvers. A synthesis o f frame-by-frame video analysis and a vector quantization algorithm, allowe d to define FIT signatures associated with 14 different types of tool/tissu e interactions. The magnitude of F/T applied by NS and ES were significantl y different(p < 0.05) and varied based on the task being performed. High F/ T magnitudes were applied by NS compared with ES while performing tissue ma nipulation and vise versa in tasks involved tissue dissection. From each st ep of the surgical procedures, two MMs were developed representing the perf ormance of three surgeons out of the five in the ES and NS groups, The data obtained by the remaining two surgeons in each group were used for evaluat ing the performance scale. The final result was a surgical performance inde x which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES, The difference between the performanc e index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES gr oups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS, However in these cases the performance index values were very close to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and FIT signatures provides an objective m eans of distinguishing NS from ES, In addition, this methodology can be fur ther applied to evaluate haptic virtual reality surgical simulators for imp roving realism in surgical education.