Evaluation of robotic minimally invasive surgical skills using motion studies

Abstract

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-andplace) 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.

Impacts of Telemanipulation in Robotic Assisted Surgery

“…In HCI, previous work have investigated how surgeons collaboratively construct a mental image of the body [43,45,46] and how they control medical equipment [44]. Specifically in robotic-assisted surgery, work has focused on training [9,28], performance metrics [31,38] and identifying future challenges in telesurgery: surgery with distributed medical teams. These challenges include remote intervention planning, awareness of remote activities, mediated communication, and the relation with the remote team and patient given their unfamiliarity with the surgeon [17,18].…”

Robotic Minimally Invasive Surgical skill assessment based on automated video-analysis motion studies

“…The performance of automated Therblig recognition was studied using misclassification rate and confusion matrix that provided reasonable estimates for accuracy as well as sensitivity of our algorithm. This classification method were also found to be capable for in real surgical analyzes [33]. Collection of video data from comprehensive subject studies will enable us to augment this classification method under probabilistic framework as well as achieve more uniform performance across different levels of expertise.…”

“…In order to anonymize the subject information, the following symbols were assigned during our analysis -experts (E 1 and E 2 ) , intermediates (11 and 1 2 ) and novices (N 1and N 2 ) . A detailed discussion of manual Therblig Analysis, dexterity and defective motion detection and its applicability in realrobotic-surgical scenarios is available in [33] and hence, only representative results are shown in Fig. 5 and Table 2.…”

An automatic skill evaluation framework for robotic surgery training

“…Meanwhile, they have compared the similarity of different expertise levels in each surgeme, and some surgemes have been found to be more indicative of skill level. Jun et al 28 have segmented two tasks (peg board and pick-and-place) into micromotions called "therbligs. "…”