Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions.

BACKGROUND: Healthcare simulators have been demonstrated to be a valuable resource for training several technical and nontechnical skills. A gap in the fidelity of tissues has been acknowledged as a barrier to application for current simulators; especially for interventional procedures. Inaccurate or unrealistic mechanical response of a simulated tissue to a given surgical tool motion may result in negative training transfer and/or prevents the "suspension of disbelief" necessary for a trainee to engage in the activity. Thus, where it is relevant to training outcomes, there should be an effort to create healthcare simulators with simulated tissue mechanical responses that match or represent those of biological tissues. Historically, this data is most often gathered from preserved (post mortem) tissue; however, there is a concern that the mechanical properties of preserved tissue, that lacks blood flow, may lack adequate accuracy to provide the necessary training efficacy of simulators. METHODS AND FINDINGS: This work explores the effect of the "state" of the tissue testing status on liver and peritoneal tissue by using a customized handheld grasper to measure the mechanical responses of representative porcine (Sus domesticus) tissues in n = 5 animals across five test conditions: in vivo, post mortem (in-situ), ex vivo (immediately removed from fresh porcine cadaver), post-refrigeration, and post-freeze-thaw cycle spanning up to 72 hours after death. No statistically significant difference was observed in the mechanical responses due to grasping between in vivo and post-freeze conditions for porcine liver and peritoneum tissue samples (p = 0.05 for derived stiffness at grasping force values F = 5N and 6.5N). Furthermore, variance between in vivo and post-freeze conditions within each animal, was comparable to the variance of the in vivo condition between animals. CONCLUSIONS: Results of this study further validate the use of preserved tissue in the design of medical simulators via observing tissue mechanical responses of post-freeze tissue comparable to in vivo tissue. Therefore, the use of thawed preserved tissue for the further study and emulation of mechanical perturbation of the liver and peritoneum can be considered. Further work in this area should investigate these trends further, particularly in regard to other tissues and the potential effects varying preservation methods may yield.

Synthetic growth by self-lubricated photopolymerization and extrusion inspired by plants and fungi.

Many natural organisms, such as fungal hyphae and plant roots, grow at their tips, enabling the generation of complex bodies composed of natural materials as well as dexterous movement and exploration. Tip growth presents an exemplary process by which materials synthesis and actuation are coupled, providing a blueprint for how growth could be realized in a synthetic system. Herein, we identify three underlying principles essential to tip-based growth of biological organisms: a fluid pressure driving force, localized polymerization for generating structure, and fluid-mediated transport of constituent materials. In this work, these evolved features inspire a synthetic materials growth process called extrusion by self-lubricated interface photopolymerization (E-SLIP), which can continuously fabricate solid profiled polymer parts with tunable mechanical properties from liquid precursors. To demonstrate the utility of E-SLIP, we create a tip-growing soft robot, outline its fundamental governing principles, and highlight its capabilities for growth at speeds up to 12 cm/min and lengths up to 1.5 m. This growing soft robot is capable of executing a range of tasks, including exploration, burrowing, and traversing tortuous paths, which highlight the potential for synthetic growth as a platform for on-demand manufacturing of infrastructure, exploration, and sensing in a variety of environments.

Virtual Reality Warm-up Before Robot-assisted Surgery: A Randomized Controlled Trial.

TRIAL DESIGN: This was a randomized controlled trial. BACKGROUND: Intraoperative errors correlate with surgeon skill and skill declines with intervals of inactivity. The goals of this research were to identify the optimal virtual reality (VR) warm-up curriculum to prime a surgeon's technical skill and validate benefit in the operating room. MATERIALS AND METHODS: Surgeons were randomized to receive six trial sessions of a designated set of VR modules on the da Vinci Skills Simulator to identify optimal VR warm-up curricula to prime technical skill. After performing their curricula, warm-up effect was assessed based on performance on a criterion task. The optimal warm-up curriculum was chosen from the group with the best task time and video review-based technical skill. Robot-assisted surgery-experienced surgeons were then recruited to either receive or not receive warm-up before surgery. Skill in the first 15 min of surgery was assessed by blinded surgeon and crowdworker review as well as tool motion metrics. The intervention was performing VR warm-up before human robot-assisted surgery. Warm-up effect was measured using objective performance metrics and video review using the Global Evaluative Assessment of Robotic Skills tool. Linear mixed effects models with a random intercept for each surgeon and nonparametric modified Friedman tests were used for analysis. RESULTS: The group performing only a Running Suture task on the simulator was on average 31.3 s faster than groups performing other simulation tasks and had the highest Global Evaluative Assessment of Robotic Skills scores from 41 surgeons who participated. This was chosen as the optimal curriculum. Thereafter, 34 surgeons completed 347 surgeries with corresponding video and tool motion data. No statistically significant differences in skill were observed with the warm-up intervention. CONCLUSIONS: We conclude that a robotic VR warm-up before performing the early stages of surgery does not impact the technical skill of the surgeon.

Crowd-Sourced Reliability of an Assessment of Lower Facial Aging Using a Validated Visual Scale.

Reliable and valid assessments of the visual endpoints of aesthetic surgery procedures are needed. Currently, most assessments are based on the opinion of patients and their plastic surgeons. The objective of this research was to analyze the reliability of crowdworkers assessing de-identified photographs using a validated scale that depicts lower facial aging. METHODS: Twenty photographs of the facial nasolabial region of various non-identifiable faces were obtained for which various degrees of facial aging were present. Independent crowds of 100 crowd workers were tasked with assessing the degree of aging using a photograph numeric scale. Independent groups of crowdworkers were surveyed at 4 different times (weekday daytime, weekday nighttime, weekend daytime, weekend nighttime), once a week for 2 weeks. RESULTS: Crowds assessing midface region photographs had an overall correlation of R = 0.979 (weekday daytime R = 0.991; weekday nighttime R = 0.985; weekend daytime R = 0.997; weekend nighttime R = 0.985). Bland-Altman test for test-retest agreement showed a normal distribution of assessments over the various times tested, with the differences in the majority of photographs being within 1 SD of the average difference in ratings. CONCLUSIONS: Crowd assessments of facial aging in de-identified photographs displayed very strong concordance with each other, regardless of time of day or week. This shows promise toward obtaining reliable assessments of pre and postoperative results for aesthetic surgery procedures. More work must be done to quantify the reliability of assessments for other pretreatment states or the corresponding results following treatment.

Bidirectional long short-term memory for surgical skill classification of temporally segmented tasks.

PURPOSE: The majority of historical surgical skill research typically analyzes holistic summary task-level metrics to create a skill classification for a performance. Recent advances in machine learning allow time series classification at the sub-task level, allowing predictions on segments of tasks, which could improve task-level technical skill assessment. METHODS: A bidirectional long short-term memory (LSTM) network was used with 8-s windows of multidimensional time-series data from the Basic Laparoscopic Urologic Skills dataset. The network was trained on experts and novices from four common surgical tasks. Stratified cross-validation with regularization was used to avoid overfitting. The misclassified cases were re-submitted for surgical technical skill assessment to crowds using Amazon Mechanical Turk to re-evaluate and to analyze the level of agreement with previous scores. RESULTS: Performance was best for the suturing task, with 96.88% accuracy at predicting whether a performance was an expert or novice, with 1 misclassification, when compared to previously obtained crowd evaluations. When compared with expert surgeon ratings, the LSTM predictions resulted in a Spearman coefficient of 0.89 for suturing tasks. When crowds re-evaluated misclassified performances, it was found that for all 5 misclassified cases from peg transfer and suturing tasks, the crowds agreed more with our LSTM model than with the previously obtained crowd scores. CONCLUSION: The technique presented shows results not incomparable with labels which would be obtained from crowd-sourced labels of surgical tasks. However, these results bring about questions of the reliability of crowd sourced labels in videos of surgical tasks. We, as a research community, should take a closer look at crowd labeling with higher scrutiny, systematically look at biases, and quantify label noise.

Temporal variability of surgical technical skill perception in real robotic surgery.

PURPOSE: Summary score metrics, either from crowds of non-experts, faculty surgeons or from automated performance metrics, have been trusted as the prevailing method of reporting surgeon technical skill. The aim of this paper is to learn whether there exist significant fluctuations in the technical skill assessments of a surgeon throughout long durations of surgical footage. METHODS: A set of 12 videos of robotic surgery cases from common human patient robotic surgeries were used to evaluate the perceived technical skill at each individual minute of the surgical videos, which were originally 12-15 min in length. A linear mixed-effects model for each video was used to compare the ratings of each minute to those from every other minute in order to learn whether a change in scores over time can be detected and reliably measured apart from inter- and intrarater variation. RESULTS: Modeling the change over time of the global evaluative assessment of robotic skills scores significantly contributed to the prediction models for 11 of the 12 surgeons. This demonstrates that measurable changes in technical skill occur over time during robotic surgery. CONCLUSION: The findings from this research raise questions about the optimal duration of footage needed to be evaluated to arrive at an accurate rating of surgical technical skill for longer procedures. This may imply non-negligible label noise for supervised machine learning approaches. In the future, it may be necessary to report a surgeon's skill variability in addition to their mean score to have proper knowledge of a surgeon's overall skill level.

The effect of video playback speed on surgeon technical skill perception.

PURPOSE:  Finding effective methods of discriminating surgeon technical skill has proved a complex problem to solve computationally. Previous research has shown that obtaining non-expert crowd evaluations of surgical performances is as accurate as the gold standard, expert surgeon review. The aim of this research is: (1) to learn whether crowdsourced evaluators give higher ratings of technical skill to video of performances with increased playback speed, (2) its effect in discriminating skill levels, and (3) whether this increase is related to the evaluator consciously being aware that the video is manually manipulated. METHODS:  A set of ten peg transfer videos (five novices, five experts) were used to evaluate the perceived technical skill of the performers at each video playback speed used ([Formula: see text]). Objective metrics used for measuring technical skill were also computed for comparison by manipulating the corresponding kinematic data of each performance. Two videos of an expert and novice performing dry laboratory laparoscopic trials of peg transfer tasks were used to obtain evaluations at each playback speed ([Formula: see text]) of perception of whether a video is played at real-time playback speed or not. RESULTS:  We found that while both novices and experts had increased perceived technical skill as the video playback was increased, the amount of increase was significantly greater for experts. Each increase in the playback speed by [Formula: see text] was associated with, on average, a 0.72-point increase in the GOALS score (95% CI 0.60-0.84 point increase; [Formula: see text]) for expert videos and only a 0.24-point increase in the GOALS score (95% CI 0.13-0.36 point increase; [Formula: see text]) for novice videos. CONCLUSION:  Due to the differential increase in perceived technical skill due to increased playback speed for experts, the difference between novice and expert skill levels of surgical performances may be more easily discerned by manually increasing the video playback speed.

Conditions for reliable grip force and jaw angle estimation of da Vinci surgical tools.

PURPOSE: This work presents an estimation technique as well as corresponding conditions which are necessary to produce an accurate estimate of grip force and jaw angle on a da Vinci surgical tool using back-end sensors alone. METHODS: This work utilizes an artificial neural network as the regression estimator on a dataset acquired from custom hardware on the proximal and distal ends. Through a series of experiments, we test the effect of estimation accuracy due to change in operating frequency, using the opposite jaw, and using different tools. A case study is then presented comparing our estimation technique with direct measurements of material response curves on two synthetic tissue surrogates. RESULTS: We establish the following criteria as necessary to produce an accurate estimate: operate within training frequency bounds, use the same side jaw, and use the same tool. Under these criteria, an average root mean square error of 1.04 mN m in grip force and 0.17 degrees in jaw angle is achieved. Additionally, applying these criteria in the case study resulted in direct measurements which fell within the 95% confidence bands of our estimation technique. CONCLUSION: Our estimation technique, along with important training criteria, is presented herein to further improve the literature pertaining to grip force estimation. We propose the training criteria to begin establishing bounds on the applicability of estimation techniques used for grip force estimation for eventual translation into clinical practice.

Blended shared control utilizing online identification : Regulating grasping forces of a surrogate surgical grasper.

PURPOSE: Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries. METHODS: This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme. RESULTS: The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of [Formula: see text] ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online. CONCLUSION: The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.

Biomechanics of human parietal pleura in uniaxial extension.

Tension pneumothorax, a major preventable cause of battlefield death, often arises from chest trauma and is treated by needle decompression to release trapped air from the pleural cavity. Surgical simulation mannequins are often employed to train medical personnel to perform this procedure properly. Accurate reproduction of the mechanical behavior of the parietal pleura, especially in response to needle penetration, is essential to maximize the fidelity of these surgical simulators. To date, however, the design of pleura-simulating material has been largely empirical and based on subjective practitioner feel rather than on the tissue properties, which have remained unknown. In this study, we performed uniaxial extension tests on samples of cadaveric human parietal pleura. We found that the pleura was highly nonlinear and mildly anisotropic, being roughly twice as stiff in the direction parallel to the ribs vs. perpendicular to the ribs (large-strain modulus = 20.44 vs. 11.49MPa). We also did not find significant correlations for most pleural properties with age or BMI, but it must be recognized that the age range (59 ± 9.5 yrs) and BMI range (31 ± 5.3) of the donors in our study was not what one might expect from combatants, and there could be differences for younger, lighter individuals. We found a significantly higher low-strain modulus in the diabetic donors (0.213 vs. 0.100MPa), consistent with the general tendency of tissue to stiffen in diabetes. The nonlinearity and tensile strength should be considered in material design and selection for future surgical simulators.

Predicting surgical skill from the first N seconds of a task: value over task time using the isogony principle.

PURPOSE: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery. METHODS: Features calculated from the isogony principle are used to train four common machine learning algorithms from dry-lab laparoscopic data gathered from three common training exercises. These models are used to predict the binary or ternary skill level of a surgeon. K-fold and leave-one-user-out cross-validation are used to assess the accuracy of the generated models. RESULTS: It is shown that the proposed scalar features can be trained to create 2-class and 3-class classification models that map to fundamentals of laparoscopic surgery skill level with median 85 and 63% accuracy in cross-validation, respectively, for the targeted dataset. Also, it is shown that the 2-class models can discern class at 90% of best-case mean accuracy with only 8 s of data from the start of the task. CONCLUSION: Novice and expert skill levels of unobserved trials can be discerned using a state vector machine trained with parameters based on the isogony principle. The accuracy of this classification comes within 90% of the classification accuracy from observing the full trial within 10 s of task initiation on average.

The minimally acceptable classification criterion for surgical skill: intent vectors and separability of raw motion data.

PURPOSE: Minimally invasive surgery requires objective methods for skill evaluation and training. This work presents the minimally acceptable classification (MAC) criterion for computational surgery: Given an obvious novice and an obvious expert, a surgical skill evaluation classifier must yield 100% accuracy. We propose that a rigorous motion analysis algorithm must meet this minimal benchmark in order to justify its cost and use. METHODS: We use this benchmark to investigate two concepts: First, how separable is raw, multidimensional dry laboratory laparoscopic motion data between obvious novices and obvious experts? We utilized information theoretic techniques to analytically address this. Second, we examined the use of intent vectors to classify surgical skill using three FLS tasks. RESULTS: We found that raw motion data alone are not sufficient to classify skill level; however, the intent vector approach is successful in classifying surgical skill level for certain tasks according to the MAC criterion. For a pattern cutting task, this approach yields 100% accuracy in leave-one-user-out cross-validation. CONCLUSION: Compared to prior art, the intent vector approach provides a generalized method to assess laparoscopic surgical skill using basic motion segments and passes the MAC criterion for some but not all FLS tasks.

Crowd-Sourced Assessment of Technical Skills for Validation of Basic Laparoscopic Urologic Skills Tasks.

PURPOSE: The BLUS (Basic Laparoscopic Urologic Skills) consortium sought to address the construct validity of BLUS tasks and the wider problem of accurate, scalable and affordable skill evaluation by investigating the concordance of 2 novel candidate methods with faculty panel scores, those of automated motion metrics and crowdsourcing. MATERIALS AND METHODS: A faculty panel of surgeons (5) and anonymous crowdworkers blindly reviewed a randomized sequence of a representative sample of 24 videos (12 pegboard and 12 suturing) extracted from the BLUS validation study (454) using the GOALS (Global Objective Assessment of Laparoscopic Skills) survey tool with appended pass-fail anchors via the same web based user interface. Pre-recorded motion metrics (tool path length, jerk cost etc) were available for each video. Cronbach's alpha, Pearson's R and ROC with AUC statistics were used to evaluate concordance between continuous scores, and as pass-fail criteria among the 3 groups of faculty, crowds and motion metrics. RESULTS: Crowdworkers provided 1,840 ratings in approximately 48 hours, 60 times faster than the faculty panel. The inter-rater reliability of mean expert and crowd ratings was good (α=0.826). Crowd score derived pass-fail resulted in 96.9% AUC (95% CI 90.3-100; positive predictive value 100%, negative predictive value 89%). Motion metrics and crowd scores provided similar or nearly identical concordance with faculty panel ratings and pass-fail decisions. CONCLUSIONS: The concordance of crowdsourcing with faculty panels and speed of reviews is sufficiently high to merit its further investigation alongside automated motion metrics. The overall agreement among faculty, motion metrics and crowdworkers provides evidence in support of the construct validity for 2 of the 4 BLUS tasks.

Validation of the AUA BLUS Tasks.

PURPOSE: Standardized assessment of laparoscopic skill in urology is lacking. We investigated whether the AUA (American Urological Association) BLUS (Basic Laparoscopic Urologic Skills) skill tasks are valid to address this need. MATERIALS AND METHODS: This institutional review board approved study included 27 medical students, 42 urology residents, 18 fellows and 37 faculty urologists across 8 sites. Using the EDGE (Electronic Data Generation and Evaluation) device (Simulab, Seattle, Washington) 454 recordings were collected on peg transfer, pattern cutting, suturing and clip applying tasks, which together comprise the expert determined BLUS tasks. We collected synchronized video and tool motion data for each trial. For each task errors, time, path length, economy of motion, peak grasp force and EDGE score were collected. An expert panel of 5 faculty members performed GOALS (Global Objective Assessment of Laparoscopic Skills) evaluations on a representative subset of peg transfer and suturing skill tasks performed by 24 participants (IRR = 0.95). RESULTS: Demographically derived skill levels proved unsuitable to evaluate construct validity. Separation of mean scores by grouped skill levels was strongest for the suturing task. Objective motion metrics and errors supported construct validity vis-à-vis correlation with blinded expert video ratings (motion metrics R(2) = 0.95, p <0.01). Expert scores appeared to reward errors in suturing but not in block transfer. CONCLUSIONS: BLUS skill task performance scoring can discriminate among basic laparoscopic technical skill levels. Self-reported demographics are an unreliable source of determining laparoscopic technical skill.

Crowd-Sourced Assessment of Technical Skill: A Valid Method for Discriminating Basic Robotic Surgery Skills.

BACKGROUND: A surgeon's skill in the operating room has been shown to correlate with a patient's clinical outcome. The prompt accurate assessment of surgical skill remains a challenge, in part, because expert faculty reviewers are often unavailable. By harnessing the power of large readily available crowds through the Internet, rapid, accurate, and low-cost assessments may be achieved. We hypothesized that assessments provided by crowd workers highly correlate with expert surgeons' assessments. MATERIALS AND METHODS: A group of 49 surgeons from two hospitals performed two dry-laboratory robotic surgical skill assessment tasks. The performance of these tasks was video recorded and posted online for evaluation using Amazon Mechanical Turk. The surgical tasks in each video were graded by (n=30) varying crowd workers and (n=3) experts using a modified global evaluative assessment of Robotic Skills (GEARS) grading tool, and the mean scores were compared using Cronbach's alpha statistic. RESULTS: GEARS evaluations from the crowd were obtained for each video and task and compared with the GEARS ratings from the expert surgeons. The crowd-based performance scores agreed with the performance assessments by experts with a Cronbach's alpha of 0.84 and 0.92 for the two tasks, respectively. CONCLUSION: The assessment of surgical skill by crowd workers resulted in a high degree of agreement with the scores provided by expert surgeons in the evaluation of basic robotic surgical dry-laboratory tasks. Crowd responses cost less and were much faster to acquire. This study provides evidence that crowds may provide an adjunctive method for rapidly providing feedback of skills to training and practicing surgeons.

Crowd-Sourced Assessment of Technical Skills: Differentiating Animate Surgical Skill Through the Wisdom of Crowds.

BACKGROUND: Objective quantification of surgical skill is imperative as we enter a healthcare environment of quality improvement and performance-based reimbursement. The gold standard tools are infrequently used due to time-intensiveness, cost inefficiency, and lack of standard practices. We hypothesized that valid performance scores of surgical skill can be obtained through crowdsourcing. METHODS: Twelve surgeons of varying robotic surgical experience performed live porcine robot-assisted urinary bladder closures. Blinded video-recorded performances were scored by expert surgeon graders and by Amazon's Mechanical Turk crowdsourcing crowd workers using the Global Evaluative Assessment of Robotic Skills tool assessing five technical skills domains. Seven expert graders and 50 unique Mechanical Turkers (each paid $0.75/survey) evaluated each video. Global assessment scores were analyzed for correlation and agreement. RESULTS: Six hundred Mechanical Turkers completed the surveys in less than 5 hours, while seven surgeon graders took 14 days. The duration of video clips ranged from 2 to 11 minutes. The correlation coefficient between the Turkers' and expert graders' scores was 0.95 and Cronbach's Alpha was 0.93. Inter-rater reliability among the surgeon graders was 0.89. CONCLUSION: Crowdsourcing surgical skills assessment yielded rapid inexpensive agreement with global performance scores given by expert surgeon graders. The crowdsourcing method may provide surgical educators and medical institutions with a boundless number of procedural skills assessors to efficiently quantify technical skills for use in trainee advancement and hospital quality improvement.

Crowd-sourced assessment of technical skills: an adjunct to urology resident surgical simulation training.

BACKGROUND: Crowdsourcing is the practice of obtaining services from a large group of people, typically an online community. Validated methods of evaluating surgical video are time-intensive, expensive, and involve participation of multiple expert surgeons. We sought to obtain valid performance scores of urologic trainees and faculty on a dry-laboratory robotic surgery task module by using crowdsourcing through a web-based grading tool called Crowd Sourced Assessment of Technical Skill (CSATS). METHODS: IRB approval was granted to test the technical skills grading accuracy of Amazon.com Mechanical Turk™ crowd-workers compared to three expert faculty surgeon graders. The two groups assessed dry-laboratory robotic surgical suturing performances of three urology residents (PGY-2, -4, -5) and two faculty using three performance domains from the validated Global Evaluative Assessment of Robotic Skills assessment tool. RESULTS: After an average of 2 hours 50 minutes, each of the five videos received 50 crowd-worker assessments. The inter-rater reliability (IRR) between the surgeons and crowd was 0.91 using Cronbach's alpha statistic (confidence intervals=0.20-0.92), indicating an agreement level between the two groups of "excellent." The crowds were able to discriminate the surgical level, and both the crowds and the expert faculty surgeon graders scored one senior trainee's performance above a faculty's performance. CONCLUSION: Surgery-naive crowd-workers can rapidly assess varying levels of surgical skill accurately relative to a panel of faculty raters. The crowds provided rapid feedback and were inexpensive. CSATS may be a valuable adjunct to surgical simulation training as requirements for more granular and iterative performance tracking of trainees become mandated and commonplace.

Beyond task time: automated measurement augments fundamentals of laparoscopic skills methodology.

BACKGROUND: Laparoscopic psychomotor skills are challenging to learn and objectively evaluate. The Fundamentals of Laparoscopic Skills (FLS) program provides a popular, inexpensive, widely-studied, and reported method for evaluating basic laparoscopic skills. With an emphasis on training safety before efficiency, we present data that explore the metrics in the FLS curriculum. MATERIALS AND METHODS: A multi-institutional (n = 3) cross-sectional study enrolled subjects (n = 98) of all laparoscopic skill levels to perform FLS tasks in an instrumented box trainer. Recorded task videos were postevaluated by faculty reviewers (n = 2) blinded to subject identity using a modified Objective Structured Assessment of Technical Skills (OSATS) protocol. FLS scores were computed for each completed task and compared with demographically established skill levels (training level and number of procedures), video review scoring, and objective performance metrics including path length, economy of motion, and peak grasping force. RESULTS: Three criteria used to determine expert skill, training and experience level, blinded review of performance by faculty via OSATS, and FLS scores, disagree in establishing concurrent validity for determining "true experts" in FLS tasks. FLS-scoring exhibited near-perfect correlation with task time for all three tasks (Pearson r = 0.99, 1.00, 1.00 with P <0.00000001). FLS error penalties had negligible effect on FLS scores. Peak grasping force did not correlate with task time or FLS scores. CONCLUSIONS: FLS technical skills scores presented negligible benefit beyond the measurement of task time. FLS scoring is weighted more toward speed than precision and may not significantly address poor tissue handling skills, especially regarding excessive grasping force. Categories of experience or training level may not form a suitable basis for establishing proficiency thresholds or for construct validity studies for technical skills.

Low-cost quantitative tool-tissue applied pressure indication method for surgical training and assessment in reality-based physical models.

We present a method of quantitatively measuring the pressure distribution applied to synthetic tissues by surgical tools via dye-impregnated microcapsules that rupture at specified pressures. A method utilizing pre-made indicator sheets is evaluated by force applications on synthetic bowel, and methods for creating paint-on indicator slurries were explored. A high spatial resolution of pressure intensity is demonstrated (0.1mm) and preliminary results merit further study.

Content and construct validation of a robotic surgery curriculum using an electromagnetic instrument tracker.

PURPOSE: Rapid adoption of robot-assisted surgery has outpaced our ability to train novice roboticists. Objective metrics are required to adequately assess robotic surgical skills and yet surrogates for proficiency, such as economy of motion and tool path metrics, are not readily accessible directly from the da Vinci® robot system. The trakSTAR™ Tool Tip Tracker is a widely available, cost-effective electromagnetic position sensing mechanism by which objective proficiency metrics can be quantified. We validated a robotic surgery curriculum using the trakSTAR device to objectively capture robotic task proficiency metrics. MATERIALS AND METHODS: Through an institutional review board approved study 10 subjects were recruited from 2 surgical experience groups (novice and experienced). All subjects completed 3 technical skills modules, including block transfer, intracorporeal suturing/knot tying (fundamentals of laparoscopic surgery) and ring tower transfer, using the da Vinci robot with the trakSTAR device affixed to the robotic instruments. Recorded objective metrics included task time and path length, which were used to calculate economy of motion. Student t test statistics were performed using STATA®. RESULTS: The novice and experienced groups consisted of 5 subjects each. The experienced group outperformed the novice group in all 3 tasks. Experienced surgeons described the simulator platform as useful for training and agreed with incorporating it into a residency curriculum. CONCLUSIONS: Robotic surgery curricula can be validated by an off-the-shelf instrument tracking system. This platform allows surgical educators to objectively assess trainees and may provide credentialing offices with a means of objectively assessing any surgical staff member seeking robotic surgery privileges at an institution.