Surgical Skill Assessment Using Motion Quality and Smoothness

Ghasemloonia, Ahmad; Maddahi, Yaser; Zareinia, Kourosh; Lama, Sanju; Dort, Joseph C.; Sutherland, Garnette R.

doi:10.1016/j.jsurg.2016.10.006

Cited by 74 publications

(49 citation statements)

References 27 publications

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“…6,11,12 In surgical technique, motion smoothness and tool handling are necessary for technical finesse. 4,[11][12][13] Accordingly, knowledge of tooltissue interaction forces is an important aspect of safe and efficient surgical performance. 12,14,15 Surgical simulation has revealed that more than 50% of errors by surgical trainees are attributable to excessive force.…”

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confidence: 99%

Forces of Tool-Tissue Interaction to Assess Surgical Skill Level

et al. 2018

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IMPORTANCE The application of optimal forces between surgical instruments and tissue is fundamental to surgical performance and learning. To date, this force has not been measured clinically during the performance of microsurgery. OBJECTIVES To establish a normative catalog of force profiles during the performance of surgery, to compare force variables among surgeons with different skill levels, and to evaluate whether such a force-based metric determines or differentiates skill level. DESIGN, SETTING, AND PARTICIPANTS Through installation of strain gauge sensors, a force-sensing bipolar forceps was developed, and force data were obtained from predetermined surgical tasks at the Foothills Medical Centre, University of Calgary, a tertiary care center that serves Southern Alberta, Canada. Sixteen neurosurgeons (3 groups: novice, intermediate, and experienced) performed surgery on 26 neurosurgical patients with various conditions. Normative baseline force ranges were obtained using the force profiles (mean and maximum forces and force variability) from the experienced surgeons. Standardized force profiles and force errors (high force error [HFE], low force error [LFE], and force variability error [FVE]) were analyzed and compared among surgeons with different skill levels. MAIN OUTCOMES AND MEASURES Each trial of the forceps use was termed successful or unsuccessful. The force profiles and force errors were analyzed and compared. RESULTS This study included 26 patients (10 [38%] male and 16 [62%] female; mean [SD] age, 43 [15] years) undergoing neurosurgery by 16 surgeons (6 in the novice group, 5 in the intermediate group, and 5 in the experienced group). Unsuccessful trial-incomplete significantly correlated with LFE and FVE, and unsuccessful trial-bleeding correlated with HFE and FVE. The force strengths exerted by novice surgeons were significantly higher than those of experienced surgeons (0.74 vs 0.00; P < .001), and force variability decreased from novice (0.43) to intermediate (0.28) to experienced (0.00) surgeons; however, these differences varied among surgical tasks. The rate of HFE and FVE inversely correlated with surgeon level of experience (HFE, 0.27 for novice surgeons, 0.12 for intermediate surgeons, and 0.05 for experienced surgeons; FVE, 0.16 for novice surgeons, 0.10 for intermediate surgeons, and 0.05 for experienced surgeons). The rate of LFE significantly increased in intermediate (0.12) and novice (0.10) surgeons compared with experienced surgeons (0.04; P < .001). There was no difference in LFE between intermediate and novice surgeons. Stepwise discriminant analysis revealed that combined use of these error rates could accurately discriminate the groups (87.5%). CONCLUSIONS AND RELEVANCE Force-sensing bipolar forceps and force analysis may help distinguish surgeon skill level, which is particularly important as surgical education shifts to a competency-based paradigm.

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Forces of Tool-Tissue Interaction to Assess Surgical Skill Level

et al. 2018

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“…Two movement variables were chosen to assess performance in this task. Firstly, jerk, a measure of change in acceleration over time, has previously been linked to surgical performance [ 34 , 35 ] and indicates smooth instrument movements [ 36 ]. Secondly, entropy, a measure of randomness/complexity, has been linked to skill learning and automaticity [ 37 ] and has previously been used to quantify surgical performance [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

The effect of observing novice and expert performance on acquisition of surgical skills on a robotic platform

et al. 2017

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BackgroundObservational learning plays an important role in surgical skills training, following the traditional model of learning from expertise. Recent findings have, however, highlighted the benefit of observing not only expert performance but also error-strewn performance. The aim of this study was to determine which model (novice vs. expert) would lead to the greatest benefits when learning robotically assisted surgical skills.Methods120 medical students with no prior experience of robotically-assisted surgery completed a ring-carrying training task on three occasions; baseline, post-intervention and at one-week follow-up. The observation intervention consisted of a video model performing the ring-carrying task, with participants randomly assigned to view an expert model, a novice model, a mixed expert/novice model or no observation (control group). Participants were assessed for task performance and surgical instrument control.ResultsThere were significant group differences post-intervention, with expert and novice observation groups outperforming the control group, but there were no clear group differences at a retention test one week later. There was no difference in performance between the expert-observing and error-observing groups.ConclusionsSimilar benefits were found when observing the traditional expert model or the error-strewn model, suggesting that viewing poor performance may be as beneficial as viewing expertise in the early acquisition of robotic surgical skills. Further work is required to understand, then inform, the optimal curriculum design when utilising observational learning in surgical training.

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“…Smoothness (S) is quantified based on the rate of changes in acceleration [14], which can reflect the continuity or nonintermittency of a movement.…”

Section: Skill Level Awarenessmentioning

confidence: 99%

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

Zhang

Xiao

Huang

et al. 2019

IEEE Robot. Autom. Lett.

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Master-slave control is a common form of humanrobot interaction for robotic surgery. To ensure seamless and intuitive control, a mechanism of self-adaptive motion scaling during teleoperaton is proposed in this paper. The operator can retain precise control when conducting delicate or complex manipulation, while the movement to a remote target is accelerated via adaptive motion scaling. The proposed framework consists of three components: 1) situation awareness, 2) skill level awareness, and 3) task awareness. The self-adaptive motion scaling ratio allows the operators to perform surgical tasks with high efficiency, forgoing the need of frequent clutching and instrument repositioning. The proposed framework has been verified on a da Vinci Research Kit (dVRK) to assess its usability and robustness. An in-house database is constructed for offline model training and parameter estimation, including both the kinematic data obtained from the robot and visual cues captured through the endoscope. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of control efficiency and task completion is significantly improved with the proposed framework.Index Terms-Learning and adaptive systems, telerobotics and teleoperation, medical robots and systems.

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Surgical Skill Assessment Using Motion Quality and Smoothness

Cited by 74 publications

References 27 publications

Forces of Tool-Tissue Interaction to Assess Surgical Skill Level

Forces of Tool-Tissue Interaction to Assess Surgical Skill Level

The effect of observing novice and expert performance on acquisition of surgical skills on a robotic platform

A Self-Adaptive Motion Scaling Framework for Surgical Robot Remote Control

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