Suture Sponge Research Articles

Assessment of first-touch skills in robotic surgical training using hi-Sim and the hinotori surgical robot system among surgeons and novices

PurposeSurgeons’ adaptability to robotic manipulation remains underexplored. This study evaluated the participants’ first-touch robotic training skills using the hinotori surgical robot system and its simulator (hi-Sim) to assess adaptability.MethodsWe enrolled 11 robotic surgeons (RS), 13 laparoscopic surgeons (LS), and 15 novices (N). After tutorial and training, participants performed pegboard tasks, camera and clutch operations, energizing operations, and suture sponge tasks on hi-Sim. They also completed a suture ligation task using the hinotori surgical robot system on a suture simulator. Median scores and task completion times were compared.ResultsPegboard task scores were 95.0%, 92.0%, and 91.5% for the RS, LS, and N groups, respectively, with differences between the RS group and LS and N groups. Camera and clutch operation scores were 93.1%, 49.7%, and 89.1%, respectively, showing differences between the RS group and LS and N groups. Energizing operation scores were 90.9%, 85.2%, and 95.0%, respectively, with a significant difference between the LS and N groups. Suture sponge task scores were 90.6%, 43.1%, and 46.2%, respectively, with differences between the RS group and LS and N groups. For the suture ligation task, completion times were 368 s, 666 s, and 1095 s, respectively, indicating differences among groups. Suture scores were 12, 10, and 7 points, respectively, with differences between the RS and N groups.ConclusionFirst-touch simulator-based robotic skills were partially influenced by prior robotic surgical experience, while suturing skills were affected by overall surgical experience. Thus, robotic training programs should be tailored to individual adaptability.

The impact of video gaming and playing a musical instrument on robotic surgical simulator performance - the Robotic Surgical Simulator Study (RS3)

BackgroundA personal history of video gaming has been associated with improved laparoscopic skills acquisition in some but not all studies. The impact of playing a musical instrument on laparoscopic or robotic proficiency is less investigated. MethodsThis study evaluates the impact of video gaming, playing a musical instrument, and other demographic parameters on robotic simulator proficiency in a cohort of 27 participants. Candidates performed 3 standardized exercises on a virtual reality robotic training platform, resulting in a proficiency score. After completing a detailed survey, participants repeated the standardized exercises to obtain a basic learning curve. Gaming history, playing a musical instrument, surgical experience, and demographic parameters were correlated with overall robotic proficiency and improvement during the experimental session. ResultsParticipants with previous gaming history had 33 % higher overall robotic performance scores (p = 0.02). There was no difference in performance between surgical trainees and attending physicians with high volume laparoscopic surgery experience (average of 77 laparoscopic procedures). In this small cohort, playing an instrument had no influence overall (p = 0.2), but we found a trend towards increased proficiency in those playing the piano, yielding 30 % higher scores (p = 0.14). Participants performed up to 22 % better in the second run of the "Suture Sponge" exercises (p = 0.08), indicating a steep learning curve of simulated robotic surgery. ConclusionsA history of video gaming and playing the piano is associated with improved performance on a virtual-reality robotic surgical simulator. The steep learning curve underlines the benefit of virtual robotic surgical training before performing robotic procedures on patients.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters’ opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

A Call to Evaluate Manual Dexterity of Prospective Surgical Trainees

IntroductionThis study assesses the correlation between academic grades and gross and fine motor skills in prospective surgical trainees. MethodsForty-seven General Surgery Residency applicants and 32 medical students with prospective surgical interests were recruited. Manual dexterity (MD) was assessed through six tasks: O'Connor Tweezer Dexterity Test and Minnesota Manual Dexterity Test; Peg Transfer Test Fundamentals of Laparoscopic Surgery (box); Ring and Rail, Thread the Ring and Suture Sponge (da Vinci Surgical Simulator). ResultsMedical students with higher academic scores had longer completion times for the peg transfer test (P = 0.013). Individuals who played musical instruments and perceived themselves to have “Excellent” MD and motor coordination (MC) were more likely to score higher on the Thread the Ring test (P = 0.007; P = 0.009 ,respectively). Those who perceived themselves to have “Mediocre” MD and MC performed the worst on the: O'Connor Tweezer Dexterity Test (P = 0.023). ConclusionsPreliminary data suggest that MD ability correlates with neither high United States Medical Licensing Examination scores nor high academic grades; however, previous experience playing a musical instrument and high self-ratings of MD/MC may be associated with better test performance.

Deep neural networks are effective tools for assessing performance during surgical training.

Surgical education courses and certification tests require human evaluators to assess performance. Deep neural network (DNN) methods include techniques for classifying the content of videos which may enable automated scoring of video performance. Researchers collected 254 videos of two simulation-based exercises performed by attending surgeons. The performance in each video was scored by experienced instructors and converted into three class labels-expert, intermediate, and novice. The videos were cut into 2227 10s clips for training DNNs in the Google Video Intelligence AutoML service. The DNN models matched the classifications applied by human evaluators with 83.1% accuracy for the Ring & Rail exercise and 80.8% for the Suture Sponge exercise. DNN models trained on individual exercises delivered very good results (80 + % accuracy) in matching the classifications assigned by human instructors and may eventually be able to supplement or replace human evaluators.

Is laparoscopic experience helpful in simulator based robotic training in general surgery?

To evaluate whether or not prior laparoscopic training improves performance during robotic surgery utilising DaVinci robotic skills simulator. The cross-sectional study was conducted at the Civil Hospital, Karachi, from May 4 to November 11, 2018, and comprised first year residents in Group A with no laparoscopic skills and fourth year residents doing laparoscopic cholecystectomy independently and surgical faculty members in Group B who had laparoscopic skills. Both the groups had no previous exposure to robotic surgery and skills simulator. There were 4 exercises which were repeated three times by each participant. Scoring was done using the DaVinci robotic skills simulator software. Data was analysed using SPSS 22. Of the 30 surgeons, there were 15(50%) in Group A with a mean age of 26±0.56 years, and 15(50%) in Group B with a mean age of 32 ± 9.16 Years (p<0.001). The overall mean age was 32±9.16 years (range: 25-52 years). There were 19(63.3) females in the sample compared to 11(36.6%) males. Mean scores of Ring walk 2, Peg board 2, and Suture sponge 3 were better in Group A, while mean score of Matchboard 2 was better in Group although B (p>0.05). Group B fared better in the individual scoring of Suture sponge 2 (p>0.05). Laparoscopic skills apparently did not confer any benefit while performing exercises on the DaVinci skills simulator.

See Like an Expert: Gaze-Augmented Training Enhances Skill Acquisition in a Virtual Reality Robotic Suturing Task.

Introduction: The da Vinci Skills Simulator (DVSS) is an effective platform for robotic skills training. Novel training methods using expert gaze patterns to guide trainees have demonstrated superiority to traditional instruction. Portable head-mounted eye-trackers (HMET) offer the opportunity for eye tracking technology to enhance surgical robotic simulation training. Objective: To evaluate if training guided by expert gaze patterns can improve trainee performance over standard movement training techniques during robotic simulation. Methods: Medical students were recruited and randomized into gaze training (GT, n = 9) and movement training (MT, n = 8) groups. First, the participants reviewed an instructional video, with the GT group emulating expert gaze patterns and the MT group (n = 8) standard movement-based instruction. Training consisted of 10 repetitions of "Suture Sponge 3" on the DVSS while wearing HMET; the first three repetitions were followed by group-appropriate video coaching (gaze vs movement feedback), while the remaining repetitions were without feedback. Finally, two multitasking repetitions with a secondary bell-counting task were completed. Primary outcomes included DVSS scores during training and multitasking. Secondary outcomes included metrics collected from the HMET (gaze patterns and gaze entropy). Results: Total score, efficiency, and penalties improved significantly over the training in both groups; the GT group achieved higher scores on every attempt. Total scores in the GT group were higher than the MT group postvideo review (20.3 ± 21.8 vs 3.0 ± 6.2, p = 0.047), after coaching repetitions (61.8 ± 18.8 vs 30.1 ± 26.2, p = 0.01), and at the last training attempt (73.0 ± 16.5 vs 63.1 ± 17.4, p = 0.247). During multitasking, the GT group maintained higher total scores (75 ± 10.1 vs 63.3 ± 15.3, p = 0.01), efficiency (86.3 ± 7.4 vs 77.4 ± 11.2, p = 0.009), and superior secondary task performance (error: 6.3% ± 0.06 vs 10.7% ± 0.11, p = 0.20). Gaze entropy (cognitive-load indicator) and gaze pattern analysis showed similar trends. Conclusion: Gaze-augmented training leads to more efficient movements through adoption of expert gaze patterns that withstand additional stressors.

Are basic robotic surgical skills transferable from the simulator to the operating room? A randomized, prospective, educational study

We aimed to assess the transferability of basic robotic skills from the simulator to the operating room (OR) while performing robotic-assisted radical prostatectomy (RARP). Fourteen urology residents were randomized into two groups: group A was required to practice three sessions (nine tasks each) on the simulator, whereas group B was required to practice (same nine tasks) until they reached competency. Both groups were recorded while practicing on the da Vinci Surgical Skills Simulator. Both groups were then recorded while performing bladder mobilization during RARP. Senior residents from both groups were also recorded while performing urethro-vesical anastomosis during RARP. Recordings were assessed blindly using the validated Global Evaluative Assessment of Robotic Skills (GEARS) tool by C-SATS. Spearman's correlation coefficient (rho) was used to assess correlation between GEARS scores from practice sessions on the da Vinci Simulator and the GEARS scores from bladder mobilization and urethro-vesical anastomosis during RARP. There was no difference in total GEARS scores between the two groups in the OR. Total GEARS scores for "ring and rail 2" and "suture sponge" tasks correlated with the total GEARS scores during urethro-vesical anastomosis (rho=0.86, p=0.007; rho=0.90, p=0.002, respectively). GEARS' efficiency component during "energy and dissection" task on the da Vinci Simulator correlated with GEARS' efficiency component during bladder mobilization (rho=0.62, p=0.03). GEARS' force sensitivity component during "ring and rail 2" and "dots and needles" tasks on the da Vinci Simulator correlated with GEARS' force sensitivity component during bladder mobilization (rho=0.58, p=0.047; rho =0.65, p=0.02, respectively). Objective assessments of urology residents on the da Vinci Surgical Skills Simulator tasks ring and rail 2 and suture sponge correlated with their objective assessments of bladder mobilization and urethro-vesical anastomosis. Therefore, basic robotic skills could be transferred from the simulator to the OR.

MP47-17 DEEP LEARNING MODELS TO PREDICT PSYCHOMOTOR ERRORS USING RAW KINEMATIC DATA FROM VIRTUAL REALITY SIMULATOR

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment II (MP47)1 Apr 2020MP47-17 DEEP LEARNING MODELS TO PREDICT PSYCHOMOTOR ERRORS USING RAW KINEMATIC DATA FROM VIRTUAL REALITY SIMULATOR Andrew Hung*, Aastha, Jessica Nguyen, and Yan Liu Andrew Hung*Andrew Hung* More articles by this author , Aastha, Jessica NguyenAastha, Jessica Nguyen More articles by this author , and Yan LiuYan Liu More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000902.017AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Kinematic performance metrics during robotic surgery have been linked to clinical outcomes. Thus far, the kinematic data we have analyzed, in the form of automated performance metrics (APMs), have been summarized data over specific steps of a surgical procedure and focused on economy of movement (i.e., efficiency). Herein, we evaluate for the first time raw (unprocessed) kinematic data during robotic simulation, utilizing artificial intelligence (AI) methods to predict Mimic’s composite scoring and select psychomotor errors. METHODS: Our analysis of raw kinematic data centered around a single needle driving task (“Basic Suture Sponge”) on the Mimic Technologies FlexVR platform. Eleven participants (surgeons/non-surgeons) completed the simulation exercise 5-11 times each. For each exercise, spatial x,y,z coordinates of the camera and instruments were collected at 30 Hz. This logged data was then used to infer “micro-displacements” along each degree of freedom and overall spatial-temporal instrument trajectory during the exercise. Psychomotor errors and composite “M Score” were reported by the simulator. We utilized several sequential deep learning algorithms, including Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) networks to map the raw data and to predict the composite score and general surgical skills: needle targeting and instrument collisions. RESULTS: 60 simulation sessions were divided into 37, 12, and 11 - training, validation, and testing cohorts, respectively. The mean ±SD composite “M score” was 925.75 ±324.85. Predicting composite simulation score: the LSTM network was able to best predict the composite “M score” with a mean absolute error 273.09 (29%). Predicting Mistargeting of Needle: GRU and RNN were able to learn best from the dataset (accuracy=76%; p=0.38), achieving 6% more accuracy than the base ratio of classes in the dataset (30% perfect/near perfect needle targeting [0-1 misses] vs. 70% imperfect targeting [>1 misses]). Predicting Instrument Collision: RNN outperformed every other model in this exercise with 64.7% accuracy (11.1% more than the base classifier, p=0.18). LSTMs and GRUs were up next with 60% and 58.8% accuracy. CONCLUSIONS: Our preliminary results suggest that the “micro-displacements data” captured during a simulation exercise can at minimum moderately capture aspects of suturing psychomotor skill. With further optimization, AI methods may be able to automate psychomotor skills assessment. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e691-e691 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Hung* More articles by this author Aastha, Jessica Nguyen More articles by this author Yan Liu More articles by this author Expand All Advertisement PDF downloadLoading ...

Gender differences in the acquisition of suturing skills with the da Vinci surgical system

The percentage of female medical students has been significant elevating worldwide. The demographic shift is expected to influence the proportion of male versus female surgeons soon. The objective of this study was to evaluate the gender differences in the acquisition of robotic suturing skills. We compared the robotic suturing performance between 39 male and 19 female medical students. We separated the training into two parts: phase I, involving virtual reality (VR) robotic simulation, and phase II, involving robotic dry-laboratory simulation training. Participants first conducted step-by-step exercises on the VR robotic simulator and then the robotic skin-suturing pad using the da Vinci robot. The metric analysis of the VR task "suture sponge" showed that female students required less time (difference: -170.7 seconds, 95% CI: -247.4 to -94.0) and had fewer errors (error difference: -50, 95% CI: -74.2 to -25.8) to complete the suture sponge exercise compared to male students. Moreover, female students completed more stitches than male students (differences in mean stitch achieved: .35; 95% CI: .06 to .65). However, there was no difference in the quality scores of stitches by gender (p=0.85). Female medical students performed better in the VR task of suture spongy and achieved more stitches than male students with the da Vinci system despite no difference in robotic suture quality by gender. Because this is the first study comparing gender performance on a robotic platform, further studies are required to investigate if different training approaches will affect the performance by gender.

Analysis of Gripping Force on a Master Controller During Simulated Robotic Surgery

Background: Although robotic surgery can improve ergonomics, strain in the hand and wrist is commonly reported. We evaluated gripping force differences between novice and expert surgeons while performing a da Vinci surgical simulator task. Methods: By attaching two force sensors to both master controllers, the average gripping force (AF) and maximum gripping force (MF) were evaluated. Participants (11 experts and 10 novices) performed the Suture sponge 1 task. Results: Almost all AF and MF in the dominant hand of experts were significantly smaller than those of novices. Mean gripping force with the dominant hand was correlated with the number of missed targets (r = 0.63, p = 0.002). Conclusions: Experts applied softer gripping force with the dominant hand than novices. Increased AF in the dominant hand was associated with more missed targets. These results may support learners to relax while performing robotic surgical procedures.

The Role of Active Engagement of Peer Observation in the Acquisition of Surgical Skills in Virtual Reality Tasks for Novices

Peer-assisted learning has been regarded as an adjunct to teaching modalities. It remains inconclusive regarding the benefits of peer observation in skills learning. Hence, we investigated whether the active engagement (AE) of peer observation in addition to expert demonstration would facilitate the performance in the virtual reality (VR) tasks. The programs involved 4 VR tasks including basic (camera targeting), intermediate (energy dissection and energy switching), and advanced (suture sponge) tasks in the da Vinci Skills Simulators, which were set up in the operating room at Taipei Medical University Hospital. Fifty medical students participated in the study. The AE of the participants was defined as the total number of peer observations in addition to expert observation before their performance. We assessed the correlations between AE and surgical task performance using Pearson correlation and the concept of learning analytics. Medical students (sixth-year students in Taiwan, equivalent to fourth-year students in the US system) from Taipei Medical University were recruited. AE was correlated with the energy dissection task (r = 0.329, p = 0.02) and marginally associated with the energy switching task (r = 0.271, p = 0.057). However, AE was not correlated with either task scores for camera targeting (r = 0.096, p = 0.509) or task scores for suture sponge (r = -0.091, p = 0.529). Our findings suggest that AE of peer observation may facilitate learning energy dissection task, which is an intermediate-level task, but not in other basic or advanced tasks in a VR context. The study highlights the potential effect of AE of peer observation on surgical learning based on a distinct level of tasks. Tasks that fit the learners' level are recommended. Nevertheless, the effectiveness of peer observation on surgical training still has to be explored to ensure favorable results and optimal learning outcomes.

MP01-02 SKILL ACQUISITION AND COGNITIVE LOAD, UTILIZING THREE DIFFERENT FORMS OF EXPERT-BASED FEEDBACK DURING SIMULATION-BASED ROBOTIC SKILLS TRAINING: A COMPARATIVE ANALYSIS

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment I1 Apr 2018MP01-02 SKILL ACQUISITION AND COGNITIVE LOAD, UTILIZING THREE DIFFERENT FORMS OF EXPERT-BASED FEEDBACK DURING SIMULATION-BASED ROBOTIC SKILLS TRAINING: A COMPARATIVE ANALYSIS Prabhakar Mithal, Brett Teplitz, Yongsoo Joo, Noorullah Maqsoodi, Karen Chong, Katherine Stewart, Henry Keenan, Stephen Hassig, Hongyi Kang, Scott Echternacht, Changyong Feng, and Ahmed Ghazi Prabhakar MithalPrabhakar Mithal More articles by this author , Brett TeplitzBrett Teplitz More articles by this author , Yongsoo JooYongsoo Joo More articles by this author , Noorullah MaqsoodiNoorullah Maqsoodi More articles by this author , Karen ChongKaren Chong More articles by this author , Katherine StewartKatherine Stewart More articles by this author , Henry KeenanHenry Keenan More articles by this author , Stephen HassigStephen Hassig More articles by this author , Hongyi KangHongyi Kang More articles by this author , Scott EchternachtScott Echternacht More articles by this author , Changyong FengChangyong Feng More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.108AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Simulation training involves didactic instruction, practice and expert feedback, which consumes considerable faculty teaching time. Consequently, novel teaching methods not requiring expert in-person attendance have been explored. We investigate the effectiveness of delivering expert feedback remotely (YouTube) or self-directed training using expert demonstration videos compared to the standard in-person expert feedback, during robotic simulation training. METHODS 15 medical students were recruited after IRB approval. Students were quasi-randomized into 3 groups (online remote expert feedback, self-directed training using expert instructional videos & in-person expert feedback). Following an instructional session and pre-test, all participants completed 4 DVSSS tasks to proficiency (Pegboard 2, Camera clutch 2, Ring Rail 3, Suture sponge 3). NASA-TLX survey was completed after each training session to assess cognitive effort. Outcomes included, number of repetitions to reach proficiency and cognitive effort during training. RESULTS For simple tasks no difference was observed between feedback modalities. Complex tasks (Ring rail 3 & Suture sponge 3) required significantly fewer repetitions in both in-person and remote expert feedback compared to the self-training group using expert instructional videos (figure 1A). On the contrary, NASA-TLX scores were lower in the self-training group (figure 1B). On separate analysis of the 6 effort NASA-TLX scales a significantly higher frustration score (how discouraged, irritated participants felt during the task), temporal demand (how pressured participants felt to complete the task) and performance scale (how successful participants felt in completing the task) were seen in the remote group, in-person expert group and self-learning group respectively. CONCLUSIONS Our findings suggest that expert remote feedback using social networking was as effective as standard expert feedback in robotic skill acquisition for complex tasks. In this limited cohort, various forms of feedback negatively impacted different aspects of cognitive workload, implying that a combination of various types of feedback may optimize skill acquisition without impacting mental workload. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Prabhakar Mithal More articles by this author Brett Teplitz More articles by this author Yongsoo Joo More articles by this author Noorullah Maqsoodi More articles by this author Karen Chong More articles by this author Katherine Stewart More articles by this author Henry Keenan More articles by this author Stephen Hassig More articles by this author Hongyi Kang More articles by this author Scott Echternacht More articles by this author Changyong Feng More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Skills in minimally invasive and open surgery show limited transferability to robotic surgery: results from a prospective study.

There is limited evidence on the transferability of conventional laparoscopic and open surgical skills to robotic-assisted surgery. The primary aim of this study was to evaluate the transferability of expertise in conventional laparoscopy and open surgery to robotic-assisted surgery using the da Vinci Skills Simulator (dVSS). Secondary aims included evaluating the influence of individual participants' characteristics. Participants performed four tasks on the dVSS: Peg Board 1 (PB), Pick and Place (PP), Thread the Rings (TR), and Suture Sponge 1 (SS). Participants were classified into three groups (Novice, Intermediate, Experts) according to experience in laparoscopic and open surgery. All tasks were performed twice except for SS. Performance was assessed using the built-in scoring system. 37 medical students and 25 surgeons participated. Experts did not perform significantly better than less experienced participants on the dVSS. Specifically, with regard to laparoscopic experience, total simulator scores were: Novices 68.2 ± 28.8; Intermediates 65.1 ± 31.2; Experts 65.1 ± 30.0; p = 0.611. Regarding open surgical experience, scores were: Novices 68.6 ± 28.7; Intermediates 68.2 ± 30.8; Experts 63.2 ± 30.3; p = 0.305. Although there were some significant differences among groups for single parameters in specific tasks, there was no constant superiority of one group. Laparoscopic and open surgical Novices improved significantly in overall score and time for all three tasks (p < 0.05). Laparoscopic intermediates improved only in PP time (4.64 ± 3.42; p = 0.006), open Intermediates in PB score (11.98 ± 13.01; p = 0.025), and open Experts in PP score (6.69 ± 11.48; p = 0.048). Laparoscopic experts showed no improvement. Participants with gaming experience had better overall scores than non-gamers when comparing all second attempts (Gamer 83.62 ± 7.57; Non-Gamer 76.31 ± 12.78; p = 0.008) as well as first and second attempts together (Gamer 72.08 ± 8.86; Non-Gamer 65.45 ± 11.68; p = 0.039). Musical and sports experience showed no correlation with robotic performance. Robotic-assisted surgery requires skills distinct from conventional laparoscopy or open surgery. Basic robotic skills training prior to patient contact should be required.

A model for predicting the GEARS score from virtual reality surgical simulator metrics.

Surgical education relies heavily upon simulation. Assessment tools include robotic simulator assessments and Global Evaluative Assessment of Robotic Skills (GEARS) metrics, which have been validated. Training programs use GEARS for proficiency testing; however, it requires a trained human evaluator. Due to limited time, learners are reliant on surgical simulator feedback to improve their skills. GEARS and simulator scores have been shown to be correlated but in what capacity is unknown. Our goal is to develop a model for predicting GEARS score using simulator metrics. Linear and multivariate logistic regressions were used on previously reported data by this group. Subjects performed simple (Ring and Rail 1) and complex (Suture Sponge 1) tasks on simulators, the dV-Trainer (dVT) and the da Vinci Skills Simulator (dVSS). They were scored via simulator metrics and GEARS. A linear model for each simulator and exercise showed a positive linear correlation. Equations were developed for predicting GEARS Total Score from simulator Overall Score. Next, the effects of each individual simulator metric on the GEARS Total Score for each simulator and exercise were examined. On the dVSS, Excessive Instrument Force was significant for Ring and Rail 1 and Instrument Collision was significant for Suture Sponge 1. On the dVT, Time to Complete was significant for both exercises. Once the significant variables were identified, multivariate models were generated. Comparing the predicted GEARS Total Score from the linear model (using only simulator Overall Score) to that using the multivariate model (using the significant variables for each simulator and exercise), the results were similar. Our results suggest that trainees can use simulator Overall Score to predict GEARS Total Score using our linear regression equations. This can improve the training process for those preparing for high-stakes assessments.

Does Previous Laparoscopic Experience Influence Basic Robotic Surgical Skills?

Studies addressing the effect of laparoscopic experience on robotic skills have produced conflicting results. This study aimed to compare simulated robotic surgical tasks using the virtual reality simulator dV-Trainer between laparoscopically experienced surgeons and first-year surgical residents. A cross-sectional study. Participants completed 4 trials of the following tasks on the dV-Trainer: Peg Board 2, Ring and Rail 1, and Suture Sponge 1. Performance was recorded using a computerized built-in scoring algorithm. Scores and metrics were compared between groups 1 and 2 and between the first and subsequent trials. Hospital de Clínicas, Porto Alegre, Brazil, a tertiary care teaching hospital. Twenty laparoscopically experienced surgeons (group 1) and 20 first-year surgical residents (group 2). All participants completed the study. The overall scores for Peg Board 2 (738.04 ± 267.83 vs 730.39 ± 225.31; p = 0.57), Ring and Rail 1 (919.03 ± 242.69 vs 965.84 ± 222.96; p = 0.13), and Suture Sponge 1 (563.62 ± 185.50 vs 560.99 ± 152.71; p = 0.67) did not differ significantly between groups 1 and 2. Group 1 had better results for master workspace range in Peg Board 2 and Ring and Rail 1. Group 2 had higher scores for economy of motion in Peg Board 2 and Ring and Rail 1 and for excessive instrument force in Ring and Rail 1. In both groups, the overall scores in the third and fourth trials were significantly higher than those in the first trial. There are no significant differences in the performance of simulated robotic surgical tasks between laparoscopically experienced surgeons and laparoscopically naïve surgical residents. Some slight differences were observed in specific metrics, but these differences were not sufficient to change the final results. We may assume that laparoscopic experience should not be an essential step in the initial learning curve of robotic surgery.

The Effect of Visual-Spatial Ability on the Learning of Robot-Assisted Surgical Skills

The aim of this study was to determine the correlation of visual-spatial ability with progression along the learning curve for robotic surgical skills training. A total of 21 novice participants were recruited. All participants completed a training program consisting of 5 training sessions of 30 minutes of virtual reality (VR) simulation and 30 minutes of dry laboratory training. The VR simulation part was the subject of the present study. During VR simulation training, participants performed the basic skill exercises of Camera Targeting 1, Pick and Place, and Peg Board 1 followed by advanced skill exercises of Suture Sponge 1 and Thread the Rings. The visual-spatial ability was assessed using a mental rotation test (MRT). Pearson correlation coefficients were used to assess the relationship between the MRT score and simulator score for the aforementioned 5 tasks. Student t test was used to compare the simulator score between high- and low-MRT score groups. A median MRT score of 26/40 (range: 13-38) was observed. Approximately 19 participants completed the full curriculum but 2 did not complete "Thread the Rings" during the study period. A significant correlation was observed between the MRT score and simulator score only in "Suture Sponge 1" over the first 3 attempts (first: r = 0.584, p = 0.0054; second: r = 0.443, p = 0.0443; third: r = 0.4458, p = 0.0428). After the third attempt, this significant correlation was lost. Comparison of the score for "Suture Sponge 1" between the high-MRT and low-MRT scoring participants divided by a median MRT score of 26 also showed a significant difference in the score until the third trial. Our observations suggest that the spatial cognitive ability influences the initial learning of robotic suturing skills. Further studies are necessary to verify the usefulness of an individual's spatial ability to tailor the surgical training program.

Analysis of the posture pattern during robotic simulator tasks using an optical motion capture system

Surgeons are sometimes forced to maintain uncomfortable joint positions during robotic surgery despite the high degree of instrument maneuverability. This study aimed to use an optical motion capture system to analyze the differences in posture patterns during robotic simulator tasks between surgeons at two skill levels. Ten experienced and ten novice surgeons performed two tasks in a da Vinci Skills Simulator: Suture Sponge 1 (SP) and Tubes (TU). The participants' upper body motion during each task was captured, including the joint angles (axilla, elbow, and wrist), the percentage of time when the wrist height was lower than the elbow height (PTW), and the height of the elbow and wrist relative to the armrest. The novice group showed significantly more excess extension in both elbow angles and extension (>50°) in both wrist angles than did the experienced group. The novice group had significantly lower PTW than the experienced group on the right side in both tasks (both p<0.001), and on the left side in SP (p<0.001). Compared with the experienced group, the novice group had a significantly higher elbow relative to the armrest on the right side (SP, TU: p<0.05), and a significantly lower wrist relative to the armrest on the right side (SP, TU: p<0.05). An optical motion capture system can detect the differences in posture patterns in the positional relationship between the elbow and wrist and the joint angles of the upper limb between two groups of surgeons at different skill levels during robotic simulator tasks.

Does Robotic Surgical Simulator Performance Correlate With Surgical Skill?

To assess the relationship between robotic surgical simulation performance and the real-life surgical skill of attending surgeons. We hypothesized that simulation performance would not correlate with real-life robotic surgical skill in attending surgeons. In 2013, Birkmeyer et al. demonstrated an association between laparoscopic surgical performance as determined by expert review of video clips and surgical outcomes. Using that model of expert review, we studied the relationship between robotic simulator performance and real-life surgical skill. Ten attending robotic surgeons performed 4 tasks on the da Vinci Skills Simulator (Camera Targeting 1, Ring Walk 3, Suture Sponge 3, and Energy Dissection 3). Two video clips of a robotic-assisted operation were then recorded for each surgeon. Three expert robotic surgeons reviewed the recordings and rated surgical technique using the Global Evaluative Assessment of Robotic Skills. University of Virginia; Charlottesville, VA; tertiary hospital PARTICIPANTS: All attending surgeons who perform robotic-assisted surgery at our institution were enrolled and completed the study. The surgeons had a median of 7.25 years of robotic surgical experience with a median of 91 cases (ranging: 20-346 cases) in the last 4 years. Median scores for each simulator task were 87.5%, 39.0%, 77.5%, and 81.5%. Using Pearson's correlation, scores for each of the individual tasks correlated poorly with expert review of intraoperative performance. There was also no correlation (r = -0.0304) between overall simulation score (mean: 70.7 ± 9.6%) and expert video ratings (mean: 3.66 ± 0.32 points). There was no correlation between attending surgeons' simulator performance and expert ratings of intraoperative videos based on the Global Evaluative Assessment of Robotic Skills scale. Although novice surgeons may put considerable effort into training on robotic simulators, performance on a simulator may not correlate with attending robotic surgical performance. Development of simulation exercises that guide novice surgeons toward expert performance is needed.

Mastery Learning - does the method of learning make a difference in skills acquisition for robotic surgery?

Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training. The dV-Trainer® and the da Vinci® Surgical System (dVSS) were used to compare practice conditions. Forty-two participants were randomized into blocked and random practice groups. Each participant performed five tasks: Ring Walk, Thread the Rings, Needle Targeting, Suture Sponge and Tubes Level 2. Transfer to the dVSS was also assessed. No significant differences were observed between the two groups, except for a few instances. For example, during Ring Walk, the random group performed significantly faster than the blocked group (100.78±5.26s vs 121.59±5.26s, p<0.01). The study results do not follow the current evidence presented in the education literature. This is the first time that blocked versus random practice was tested for robotic surgery training.