A decision aid for the port placement problem in robot-assisted hysterectomy

Abstract

ObjectiveIn robot-assisted minimally invasive surgery, proper port positioning ensures that surgical tools have adequate access to the surgical site and avoids mid-surgery collisions of robotic arms. To date, several guidelines have been proposed for more accurate port placement. However, challenges remain due to variations in patient morphology, anatomy, and, in particular, organ displacement due to insufflation in certain laparoscopic procedures. The objective of this study was to design and develop a decision aid for optimal port placement in robot-assisted hysterectomy that accounts for patient variability and organ displacement due to insufflation. MethodsThree components were constructed: a robot model, a patient-specific model, and an optimization algorithm. The three components were integrated, and the system was verified using four different patients who underwent robotic hysterectomy. Once verified, two expert surgeons were asked to evaluate the decision aid for face and construct validity. A usability test was conducted using a torso phantom with target organs located in three different locations. Two expert surgeons performed a simulated hysterectomy task with and without the decision aid to evaluate performance and satisfaction. ResultsThe optimization algorithm was sensitive to individual differences in anatomy in the four patients. Expert surgeons successfully established face and construct validity. Usability test results showed a 28%–40% reduction in time to task completion with the optimized ports compared to expert-determined port locations without using the decision aid. ConclusionsThe decision aid, based on an individualized patient-specific model, robot model, and optimization algorithm, was shown to be effective at addressing the challenges of displaced organs due to insufflation in robot-assisted hysterectomy. The face and construct validity of the decision aid was established by expert surgeons, while efficiency gains in task performance were demonstrated experimentally.