UCLA

Laparoscopic, or minimally invasive surgery (MIS) has been shown to provide tremendous advantages for patients. Safe and efficient laparoscopic surgery requires advanced psychomotor skills; and novice laparoscopic surgeons face a steep and challenging learning curve to develop them. This problem is exacerbated by the lack of expert mentors who are concentrated in relatively few centers and often are not readily available to mentor novice surgeons, or to perform surgeries in person - thus creating a need for telesurgery. The goal of this dissertation is to address these challenges.

The overarching hypothesis of this dissertation is that telementoring systems in combination with machine learning algorithms and active haptic guidance can bridge the gap in learning the advanced surgical skills required for MIS.

To test this hypothesis we have developed two pieces of technology: the UCLA Laparoscopic Training Station (LTS) and the UCLA LapaRobot. Tracking the motion of surgical instruments via the UCLA–LTS, we have collected an intraoperative dataset from two separate experiments: (a) a combined phacoemulsification (PKE) and pars plana vitrectomy (PPV) procedure on a pig eyeball, and (b) a porcine laparoscopic cholecystectomy. From these datasets we have extracted a set of kinematically based performance metrics to evaluate the MIS surgical skills of novice trainees. We then conducted a construct validation test of the UCLA–LTS, where we evaluated the kinematic performance metrics from two populations of test participants, an expert group and novice. Our analysis shows that, when combined with machine learning algorithms, these performance metrics were successful in differentiating between the psychomotor skill of the expert mentors and those of novice.

We then built a prototype of the UCLA – LapaRobot and laid the foundation to demonstrate that active guidance from a haptic force feedback mechanism has the potential to facilitate the learning of MIS-specific surgical skills for remote trainees in a telementoring scenario. We conclude that we can further enhance the deployment of MIS to remote locations in a telesurgery scenario, with medic-trained personnel at the slave station, assisted by the kinesthetic force feedback of the UCLA–LapaRobot