No abstract

The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed 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 interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly 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 manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance 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 evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance 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 groups. 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 F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.

Accurate knowledge of biomechanical characteristics of tissues is essential for developing realistic computer-based surgical simulators incorporating haptic feedback, as well as for the design of surgical robots and tools. As simulation technologies continue to be capable of modeling more complex behavior, an in vivo tissue property database is needed. Most past and current biomechanical research is focused on soft and hard anatomical structures that are subject to physiological loading, testing the organs in situ. Internal organs are different in that respect since they are not subject to extensive loads as part of their regular physiological function. However, during surgery, a different set of loading conditions are imposed on these organs as a result of the interaction with the surgical tools. Following previous research studying the kinematics and dynamics of tool/tissue interaction in real surgical procedures, the focus of the current study was to obtain the structural biomechanical properties (engineering stress-strain and stress relaxation) of seven abdominal organs, including bladder, gallbladder, large and small intestines, liver, spleen, and stomach, using a porcine animal model. The organs were tested in vivo, in situ, and ex corpus (the latter two conditions being postmortem) under cyclical and step strain compressions using a motorized endoscopic grasper and a universal-testing machine. The tissues were tested with the same loading conditions commonly applied by surgeons during minimally invasive surgical procedures. Phenomenological models were developed for the various organs, testing conditions, and experimental devices. A property database-unique to the literature-has been created that contains the average elastic and relaxation model parameters measured for these tissues in vivo and postmortem. The results quantitatively indicate the significant differences between tissue properties measured in vivo and postmortem. A quantitative understanding of how the unconditioned tissue properties and model parameters are influenced by time postmortem and loading condition has been obtained. The results provide the material property foundations for developing science-based haptic surgical simulators, as well as surgical tools for manual and robotic systems.

Minimally invasive surgery generates new user interfaces which create visual and haptic distortion when compared to traditional surgery. In order to regain the tactile and kinesthetic information that is lost, a computerized force feedback endoscopic surgical grasper (FREG) was developed with computer control and a haptic user interface. The system uses standard unmodified grasper shafts and tips. The FREG can control grasping forces either by surgeon teleoperation control, or under software control. The FREG performance was evaluated using an automated palpation function (programmed series of compressions) in which the grasper measures mechanical properties of the grasped materials. The material parameters obtained from measurements showed the ability of the FREG to discriminate between different types of normal soft tissues (small bowel, lung, spleen, liver, colon, and stomach) and different kinds of artificial soft tissue replication materials (latex/silicone) for simulation purposes. In addition, subjective tests of ranking stiffness of silicone materials using the FREG teleoperation mode showed significant improvement in the performance compared to the standard endoscopic grasper. Moreover, the FREG performance was closer to the performance of the human hand than the standard endoscopic grasper. The FREG as a tool incorporating the force feedback teleoperation technology may provide the basis for application in telesurgery, clinical endoscopic surgery, surgical training, and research.

Minimally invasive surgery (MIS) involves a multidimensional series of tasks requiring a synthesis between visual information and the kinematics and dynamics of the surgical tools. Analysis of these sources of information is a key step in defining objective criteria for characterizing surgical performance. The Blue DRAGON is a new system for acquiring the kinematics and the dynamics of two endoscopic tools synchronized with the endoscopic view of the surgical scene. Modeling the process of MIS using a finite state model [Markov model (MM)] reveals the internal structure of the surgical task and is utilized as one of the key steps in objectively assessing surgical performance. The experimental protocol includes tying an intracorporeal knot in a MIS setup performed on an animal model (pig) by 30 surgeons at different levels of training including expert surgeons. An objective learning curve was defined based on measuring quantitative statistical distance (similarity) between MM of experts and MM of residents at different levels of training. The objective learning curve was similar to that of the subjective performance analysis. The MM proved to be a powerful and compact mathematical model for decomposing a complex task such as laparoscopic suturing. Systems like surgical robots or virtual reality simulators in which the kinematics and the dynamics of the surgical tool are inherently measured may benefit from incorporation of the proposed methodology.