Evaluation of robotic minimally invasive surgical skills using motion studies

摘要

Robotic minimally-invasive-surgery (rMIS) is the fastest growing segment of computer-aided surgical systems today and has often been heralded as the new revolution in healthcare industry. However, the surgical performance-evaluation paradigms have always failed to keep pace with the advances of surgical technology. In this work, we examine extension of traditional manipulative skill assessment with deep roots in performance evaluation in manufacturing industries for applicability to robotic surgical skill evaluation. This method relies on defining task-level segmentation of modular sub-procedures called "Therbligs" that can be combined to perform a given task. Performance metrics including intra- and inter-user performance variance can by analyzed by studying surgeons' performance over each sub-tasks. Additional metrics on tool-motion measurements, motion economy, and handed-symmetry can be similarly expanded over this temporal segmentation to help characterize performance. Our studies analyzed video recordings of surgical task performance in two settings: First, we examine performance of two representative manipulation exercises (peg board and pick-and-place) on a da Vinci surgical (SKILLS) simulator to afford a relatively-controlled and standardized testbed for surgeons with varied experience-levels. Second task-sequences from real surgical videos were analyzed with a list of predefined "Therbligs" in order to investigate its usefulness for real implementation.