Objective Assessment of the Early Stages of the Learning Curve for a Novel Surgical Robotic System
Background: The Senhance™ Surgical Robotic System has instrument force feedback capabilities, but the learning curve has yet to be defined. The purpose of this research is to study the early stages of the Senhance™ learning curve using instrument tracking and Fundamentals of Laparoscopic Surgery (FLS) aggregate scores and to explore if force feedback impacts learning rate.
Materials and Methods: Sixteen subjects attempted modified versions of the peg transfer and precision cutting tasks from the FLS curriculum using the Senhance™. Subjects were categorized as novices or experts based on laparoscopic surgery experience and randomly assigned to complete the tasks with the force feedback capability engaged or disengaged. Instruments were tracked for path length and aggregate scores were calculated per FLS standards as functions of time and precision.
Results: The novice and expert cohorts showed significant linear improvement for the peg transfer task, while neither showed significant linear improvement for the precision cutting task. Both force feedback engaged and disengaged cohorts showed significant linear improvement for the precision cutting task, while neither showed significant linear improvement for the peg transfer task. A significant monotonic relationship between path length and aggregate scores was found for the peg transfer task for the novice and force feedback disengaged cohorts and for the precision cutting task for the novice and both force feedback engaged and disengaged cohorts. Significant improvement was found for aggregate scores of the peg transfer task for all cohorts and total path length being significant for the novice cohort. The precision cutting task resulted in no significant improvement for aggregate scores across all cohorts, but showed total path length improvement for the expert and force feedback engaged cohorts.
Conclusions: This study has shown that surgeon learning and adaptation to the Senhance™ controls is rapid, regardless of experience level and force feedback engagement.
Measuring Skill Degradation on a Robotic Surgery Simulator
There has been a rapid increase in the number of robotic systems being used and introduced into operating rooms in recent years. Systems, such as the da Vinci Surgical System, have been used to assist in minimally invasive procedures for over a decade in surgical specialties such as general, urologic, cardiac, thoracic, and gynecologic surgery. Training surgeons to become proficient with the da Vinci is complex and challenging as no universal training requirements exist and evaluation methods that are commonly used are based on subjective measures, such as expert panel scoring of recorded tasks or procedures. We are investigating novel methods for evaluating the skill of novice users of the da Vinci Skills Simulator, a da Vinci console that has a library of virtual surgical tasks for surgeons to train and practice on. Our approach incorporates motion tracking of subjects’ fingers, hands, feet, and elbows as well as the console pedal movements to determine if features from kinematic data can be extracted and correlated to subjective expert scoring of videoed tasks. Analytically, we are applying classification algorithms to study and investigate novel and cost-effective methods for evaluating novice robotic surgeons objectively alongside current subjective methods.