Portable neuroimaging differentiates novices from those with experience for the Fundamentals of Laparoscopic Surgery (FLS) suturing with intracorporeal knot tying task.

BACKGROUND: The goal of this study was to compare the brain activation patterns of experienced and novice individuals when performing the Fundamentals of Laparoscopic Surgery (FLS) suture with intracorporeal knot tying task, which requires bimanual motor control. METHODS: Twelve experienced and fourteen novice participants completed this cross-sectional observational study. Participants performed three repetitions of the FLS suture with intracorporeal knot tying task in a standard box trainer. Functional near infrared spectroscopy (fNIRS) data was recorded using an optode montage that covered the prefrontal and sensorimotor brain areas throughout the task. Data processing was conducted using the HOMER3 and AtlasViewer toolboxes to determine the oxy-hemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. The hemodynamic response function based on HbO changes during the task relative to the resting state was averaged for each repetition and by participant. Group-level differences were evaluated using a general linear model of the HbO changes with significance set at p < 0.05. RESULTS: The average performance score for the experienced group was significantly higher than the novice group (p < 0.01). There were significant cortical activations (p < 0.05) in the prefrontal and sensorimotor areas for the experienced and novice groups. Areas of statistically significant differences in activation included the right dorsolateral prefrontal cortex (PFC), the right precentral gyrus, and the right postcentral gyrus. CONCLUSIONS: Portable neuroimaging allowed for the differentiation of brain regions activated by experienced and novice participants for a complex surgical motor task. This information can be used to support the objective evaluation of expertise during surgical skills training and assessment.

Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty.

BACKGROUND: We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques. METHODS: We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores. RESULTS: We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72. CONCLUSION: This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.

Objective metrics for hand-sewn bowel anastomoses can differentiate novice from expert surgeons.

BACKGROUND: Assessing performance automatically in a virtual reality trainer or from recorded videos is advantageous but needs validated objective metrics. The purpose of this study is to obtain expert consensus and validate task-specific metrics developed for assessing performance in double-layered end-to-end anastomosis. MATERIALS AND METHODS: Subjects were recruited into expert (PGY 4-5, colorectal surgery residents, and attendings) and novice (PGY 1-3) groups. Weighted average scores of experts for each metric item, completion time, and the total scores computed using global and task-specific metrics were computed for assessment. RESULTS: A total of 43 expert surgeons rated our task-specific metric items with weighted averages ranging from 3.33 to 4.5 on a 5-point Likert scale. A total of 20 subjects (10 novices and 10 experts) participated in validation study. The novice group completed the task significantly more slowly than the experienced group (37.67 ± 7.09 vs 25.47 ± 7.82 min, p = 0.001). In addition, both the global rating scale (23.47 ± 4.28 vs 28.3 ± 3.85, p = 0.016) and the task-specific metrics showed a significant difference in performance between the two groups (38.77 ± 2.83 vs 42.58 ± 4.56 p = 0.027) following partial least-squares (PLS) regression. Furthermore, PLS regression showed that only two metric items (Stay suture tension and Tool handling) could reliably differentiate the performance between the groups (20.41 ± 2.42 vs 24.28 ± 4.09 vs, p = 0.037). CONCLUSIONS: Our study shows that our task-specific metrics have significant discriminant validity and can be used to evaluate the technical skills for this procedure.

What are essential laparoscopic skills these days? Results of the SAGES Fundamentals of Laparoscopic Surgery (FLS) Committee technical skills survey.

INTRODUCTION: The Fundamentals of Laparoscopic Surgery (FLS) program tests basic knowledge and skills required to perform laparoscopic surgery. Educational experiences in laparoscopic training and development of associated competencies have evolved since FLS inception, making it important to review the definition of fundamental laparoscopic skills. The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) assigned an FLS Technical Skills Working Group to characterize technical skills used in basic laparoscopic surgery in current practice contexts and their possible application to future FLS tests. METHODS: A group of subject matter experts defined an inventory of 65 laparoscopic skills using a Nominal Group Technique. From these, a survey was developed rating these items for importance, frequency of use, and priority for testing for FLS certification. This survey was distributed to SAGES members, recent recipients of FLS certification, and members of the Association of Program Directors in Surgery (APDS). Results were collected using a secure web-based survey platform. RESULTS: Complete data were available for 1742 surveys. Of these, 1143 comprised results for post-residency participants who performed advanced procedures. Seventeen competencies were identified for FLS testing prioritization by determining the proportion of respondents who identified them of highest priority, at median (50th percentile) of the maximum survey scale rating. These included basic peritoneal access, laparoscope and instrument use, tissue manipulation, and specific problem management skills. Sixteen could be used to show appropriateness of the domain construct by confirmatory factor analysis. Of these 8 could be characterized as manipulative tasks. Of these 5 mapped to current FLS tasks. CONCLUSIONS: This survey-identified competencies, some of which are currently assessed in FLS, with a high level of priority for testing. Further work is needed to determine if this should prompt consideration of changes or additions to the FLS technical skills test component.

Split & Join: An Efficient Approach for Simulating Stapled Intestinal Anastomosis in Virtual Reality.

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using a new hybrid representation - a grid-linked particles model for physics simulation and a surface mesh for rendering. The proposed split and join operations handle the updates of both the grid-linked particles model and the surface mesh during the anastomosis procedure. The simulation results demonstrate the feasibility of the proposed approach in simulating intestine models and the side-to-side anastomosis operation.

Hierarchical task analysis of endoscopic sleeve gastroplasty.

BACKGROUND: Endoscopic sleeve gastroplasty (ESG) is a minimally invasive endoscopic weight loss procedure used to treat obesity. The long-term goal of this project is to develop a Virtual Bariatric Endoscopy (ViBE) simulator for training and assessment of the ESG procedure. The objectives of this current work are to: (a) perform a task analysis of ESG and (b) create metrics to be validated in the created simulator. METHODS: We performed a hierarchical task analysis (HTA) by identifying the significant tasks of the ESG procedure. We created the HTA to show the breakdown and connection of the tasks of the procedure. Utilizing the HTA and input from ESG experts, performance metrics were derived for objective measurement of the ESG procedure. Three blinded video raters analyzed seven recorded ESG procedures according to the proposed performance metrics. RESULTS: Based on the seven videos, there was a positive correlation between total task times and total performance scores (R = 0.886, P = 0.008). Endoscopists expert were found to be more skilled in reducing the area of the stomach compared to endoscopists novice (34.6% reduction versus 9.4% reduction, P = 0.01). The mean novice performance score was significantly lower than the mean expert performance score (34.7 vs. 23.8, P = 0.047). The inter-rater reliability test showed a perfect agreement among three raters for all tasks except for the suturing task. The suturing task had a significant agreement (Inter-rater Correlation = 0.84, Cronbach's alpha = 0.88). Suturing was determined to be a critical task that is positively correlated with the total score (R = 0.962, P = 0.0005). CONCLUSION: The task analysis and metrics development are critical for the development of the ViBE simulator. This preliminary assessment demonstrates that the performance metrics provide an accurate assessment of the endoscopist's performance. Further validation testing and refinement of the performance metrics are anticipated.

Does your team know how to respond safely to an operating room fire? Outcomes of a virtual reality, AI-enhanced simulation training.

BACKGROUND: Operating room (OR) fires are rare but devastating events requiring immediate and effective response. Virtual Reality (VR) simulation training can provide a safe environment for practice of skills in such highly stressful situation. This study assessed interprofessional participants' ability to respond to VR-simulated OR fire scenarios, attitudes, numbers of attempt of the VR simulation do participants need to successfully respond to OR fires and does prior experience, confidence level, or professional role predict the number of attempts needed to demonstrate safety and pass the simulation. METHODS: 180 surgical team members volunteered to participate in this study at Beth Israel Deaconess Medical Center, Boston, MA. Each participant completed five VR OR simulation trials; the final two trials incorporated AI assistance. Primary outcomes were performance scores, number of attempts needed to pass, and pre- and post-survey results describing participant confidence and experiences. Differences across professional or training role were assessed using chi-square tests and analyses of variance. Differences in pass rates over time were assessed using repeated measures logistic regression. RESULTS: One hundred eighty participants completed simulation testing; 170 (94.4%) completed surveys. Participants included surgeons (17.2%), anesthesiologists (10.0%), allied health professionals (41.7%), and medical trainees (31.1%). Prior to training, 45.4% of participants reported feeling moderately or very confident in their ability to respond to an OR fire. Eight participants (4.4%) responded safely on the first simulation attempt. Forty-three participants (23.9%) passed by the third attempt (VR only); an additional 97 participants (53.9%) passed within the 4-5th attempt (VR with AI assistance). CONCLUSIONS: Providers are unprepared to respond to OR fires. VR-based simulation training provides a practical platform for individuals to improve their knowledge and performance in the management of OR fires with a 79% pass rate in our study. A VR AI approach to teaching this essential skill is innovative, feasible, and effective.

Virtual reality operating room with AI guidance: design and validation of a fire scenario.

BACKGROUND: Operating room (OR) fires are uncommon but disastrous events. Inappropriate handling of OR fires can result in injuries, even death. Aiming to simulate OR fire emergencies and effectively train clinicians to react appropriately, we have developed an artificial intelligence (AI)-based OR fire virtual trainer based on the principle of the "fire triangle" and SAGES FUSE curriculum. The simulator can predict the user's actions in the virtual OR and provide them with timely feedback to assist with training. We conducted a study investigating the validity of the AI-assisted OR fire trainer at the 2019 SAGES Learning Center. METHODS: Fifty-three participants with varying medical experience were voluntarily recruited to participate in our Institutional Review Board approved study. All participants were asked to contain a fire within the virtual OR. Participants were then asked to fill out a 7-point Likert questionnaire consisting of ten questions regarding the face validation of the AI-assisted OR fire simulator. Shapiro-Wilk tests were conducted to test normality of the scores for each trial. A Friedman's ANOVA with post hoc tests was used to evaluate the effect of multiple trials on performance. RESULTS: On a 7-point scale, eight of the ten questions were rated a mean of 6 or greater (72.73%), especially those relating to the usefulness of the simulator for OR fire-containing training. 79.25% of the participants rated the degree of usefulness of AI guidance over 6 out of 7. The performance of individuals improved significantly over the five trials, χ2(4) = 119.89, p < .001, and there was a significant linear trend of performance r = .97, p = 0.006. A pairwise analysis showed that only after the introduction of AI did performance improve significantly. CONCLUSIONS: The AI-guided OR fire trainer offers the potential to assess OR personnel and teach the proper response to an iatrogenic fire scenario in a safe, repeatable, immersive environment.

Surgeons With Five or More Actual Cricothyrotomies Perform Significantly Better on a Virtual Reality Simulator.

BACKGROUND: Discriminating performance of learners with varying experience is essential to developing and validating a surgical simulator. For rare and emergent procedures such as cricothyrotomy (CCT), the criteria to establish such groups are unclear. This study is to investigate the impact of surgeons' actual CCT experience on their virtual reality simulator performance and to determine the minimum number of actual CCTs that significantly discriminates simulator scores. Our hypothesis is that surgeons who performed more actual CCT cases would perform better on a virtual reality CCT simulator. METHODS: 47 clinicians were recruited to participate in this study at the 2018 annual conference of the Society of American Gastrointestinal and Endoscopic Surgeons. We established groups based on three different experience thresholds, that is, the minimal number of CCT cases performed (1, 5, and 10), and compared simulator performance between these groups. RESULTS: Participants who had performed more clinical cases manifested higher mean scores in completing CCT simulation tasks, and those reporting at least 5 actual CCTs had significantly higher (P = 0.014) simulator scores than those who had performed fewer cases. Another interesting finding was that classifying participants based on experience level, that is, attendings, fellows, and residents, did not yield statistically significant differences in skills related to CCT. CONCLUSIONS: The simulator was sensitive to prior experience at a threshold of 5 actual CCTs performed.

Characterizing the learning curve of a virtual intracorporeal suturing simulator VBLaST-SS©.

BACKGROUND: The virtual basic laparoscopic skill trainer suturing simulator (VBLaST-SS©) was developed to simulate the intracorporeal suturing task in the FLS program. The purpose of this study was to evaluate the training effectiveness and participants' learning curves on the VBLaST-SS© and to assess whether the skills were retained after 2 weeks without training. METHODS: Fourteen medical students participated in the study. Participants were randomly assigned to two training groups (7 per group): VBLaST-SS© or FLS, based on the modality of training. Participants practiced on their assigned system for one session (30 min or up to ten repetitions) a day, 5 days a week for three consecutive weeks. Their baseline, post-test, and retention (after 2 weeks) performance were also analyzed. Participants' performance scores were calculated based on the original FLS scoring system. The cumulative summation (CUSUM) method was used to evaluate learning. Two-way mixed factorial ANOVA was used to compare the effects of group, time point (baseline, post-test, and retention), and their interaction on performance. RESULTS: Six out of seven participants in each group reached the predefined proficiency level after 7 days of training. Participants' performance improved significantly (p < 0.001) after training within their assigned group. The CUSUM learning curve shows that one participant in each group achieved 5% failure rate by the end of the training period. Twelve out of fourteen participants' CUSUM curves showed a negative trend toward achieving the 5% failure rate after further training. CONCLUSION: The VBLaST-SS© is effective in training laparoscopic suturing skill. Participants' performance of intracorporeal suturing was significantly improved after training on both systems and was retained after 2 weeks of no training.

Endoscopic submucosal dissection: a cognitive task analysis framework toward training design.

BACKGROUND: One of the major impediments to the proliferation of endoscopic submucosal dissection (ESD) training in Western countries is the lack of sufficient experts as instructors. One way to address this gap is to develop didactic systems, such as surgical simulators, to support the role of trainers. Cognitive task analysis (CTA) has been used in healthcare for the design and improvement of surgical training programs, and therefore can potentially be used for design of similar systems for ESD. OBJECTIVE: The aim of the study was to apply a CTA-based approach to identify the cognitive aspects of performing ESD, and to generate qualitative insights for training. MATERIALS AND METHODS: Semi-structured interviews were designed based on the CTA framework to elicit knowledge of ESD practitioners relating to the various tasks involved in the procedure. Three observations were conducted of expert ESD trainers either while they performed actual ESD procedures or at a training workshop. Interviews were either conducted over the phone or in person. Interview participants included four experts and four novices. The observation notes and interviews were analyzed for emergent qualitative themes and relationships. RESULTS: The qualitative analysis yielded thematic insights related to four main cognition-related categories: learning goals/principles, challenges/concerns, strategies, and decision-making. The specific insights under each of these categories were systematically mapped to the various tasks inherent to the ESD procedure. CONCLUSIONS: The CTA approach was applied to identify cognitive themes related to ESD procedural tasks. Insights developed based on the qualitative analysis of interviews and observations of ESD practitioners can be used to inform the design of ESD training systems, such as virtual reality-based simulators.

Development and face validation of a virtual camera navigation task trainer.

BACKGROUND: The fundamentals of laparoscopic surgery (FLS) trainer box, which is now established as a standard for evaluating minimally invasive surgical skills, consists of five tasks: peg transfer, pattern cutting, ligation, intra- and extracorporeal suturing. Virtual simulators of these tasks have been developed and validated as part of the Virtual Basic Laparoscopic Skill Trainer (VBLaST) (Arikatla et al. in Int J Med Robot Comput Assist Surg 10:344-355, 2014; Zhang et al. in Surg Endosc 27(10):3603-3615, 2013; Sankaranarayanan et al. in J Laparoendosc Adv Surg Tech 20(2):153-157, 2010; Qi et al. J Biomed Inform 75:48-62, 2017). The virtual task trainers have many advantages including automatic real-time objective scoring, reduced costs, and eliminating human proctors. In this paper, we extend VBLaST by adding two camera navigation system tasks: (a) pattern matching and (b) path tracing. METHODS: A comprehensive camera navigation simulator with two virtual tasks, simplified and cheaper hardware interface (compared to the prior version of VBLaST), graphical user interface, and automated metrics has been designed and developed. Face validity of the system is tested with medical students and residents from the University at Buffalo's medical school. RESULTS: The subjects rated the simulator highly in all aspects including its usefulness in training to center the target and to teach sizing skills. The quality and usefulness of the force feedback scored the lowest at 2.62.

Objective assessment of surgical skill transfer using non-invasive brain imaging.

BACKGROUND: Physical and virtual surgical simulators are increasingly being used in training technical surgical skills. However, metrics such as completion time or subjective performance checklists often show poor correlation to transfer of skills into clinical settings. We hypothesize that non-invasive brain imaging can objectively differentiate and classify surgical skill transfer, with higher accuracy than established metrics, for subjects based on motor skill levels. STUDY DESIGN: 18 medical students at University at Buffalo were randomly assigned into control, physical surgical trainer, or virtual trainer groups. Training groups practiced a surgical technical task on respective simulators for 12 consecutive days. To measure skill transfer post-training, all subjects performed the technical task in an ex-vivo environment. Cortical activation was measured using functional near-infrared spectroscopy (fNIRS) in the prefrontal cortex, primary motor cortex, and supplementary motor area, due to their direct impact on motor skill learning. RESULTS: Classification between simulator trained and untrained subjects based on traditional metrics is poor, where misclassification errors range from 20 to 41%. Conversely, fNIRS metrics can successfully classify physical or virtual trained subjects from untrained subjects with misclassification errors of 2.2% and 8.9%, respectively. More importantly, untrained subjects are successfully classified from physical or virtual simulator trained subjects with misclassification errors of 2.7% and 9.1%, respectively. CONCLUSION: fNIRS metrics are significantly more accurate than current established metrics in classifying different levels of surgical motor skill transfer. Our approach brings robustness, objectivity, and accuracy in validating the effectiveness of future surgical trainers in translating surgical skills to clinically relevant environments.

Validation of a virtual intracorporeal suturing simulator.

BACKGROUND: Intracorporeal suturing is one of the most important and difficult procedures in laparoscopic surgery. Practicing on a FLS trainer box is effective but requires large number of consumables, and the scoring is somewhat subjective and not immediate. A virtualbasic laparoscopic skill trainer (VBLaST©) was developed to simulate the five tasks of the FLS Trainer Box. The purpose of this study is to evaluate the face and content validity of the VBLaST suturing simulator (VBLaST-SS©). METHODS: Twenty-five medical students and residents completed an evaluation of the simulator. The participants were asked to perform the standard intracorporeal suturing task on both VBLaST-SS© and the traditional FLS box trainer. The performance scores on each system were calculated based on time (s), deviations to the black dots (mm), and incision gap (mm). The participants were then asked to finish a 13-item questionnaire with ratings from 1 (not realistic/useful) to 5 (very realistic/useful) regarding the face validity of the simulator. A Wilcoxon signed rank test was performed to identify differences in performance on the VBLaST-SS© compared to that of the traditional FLS box trainer. RESULTS: Three questions from the face validity questionnaire were excluded due to lack of response. Ratings to 8 of the remaining 10 questions (80%) averaged above 3.0 out of 5. Average intracorporeal suturing completion time on the VBLaST-SS© was 421 (SD = 168 s) seconds compared to 406 (175 s) seconds on the box trainer (p = 0.620). There was a significant difference between systems for the incision gap (p = 0.048). Deviation in needle insertion from the black dot was smaller for the box trainer than the virtual simulator (1.68 vs. 7.12, p < 0.001). CONCLUSION: Participants showed comparable performance on the VBLaST-SS© and traditional box trainer. Overall, the VBLaST-SS© system showed face validity and has the potential to support training for the suturing skills.

Correction to: Validation of a virtual intracorporeal suturing simulator.

The surname of Sreekanth Arikatla incorrectly appeared as Sreekanth Artikala.

A task and performance analysis of endoscopic submucosal dissection (ESD) surgery.

BACKGROUND: ESD is an endoscopic technique for en bloc resection of gastrointestinal lesions. ESD is a widely-used in Japan and throughout Asia, but not as prevalent in Europe or the US. The procedure is technically challenging and has higher adverse events (bleeding, perforation) compared to endoscopic mucosal resection. Inadequate training platforms and lack of established training curricula have restricted its wide acceptance in the US. Thus, we aim to develop a Virtual Endoluminal Surgery Simulator (VESS) for objective ESD training and assessment. In this work, we performed task and performance analysis of ESD surgeries. METHODS: We performed a detailed colorectal ESD task analysis and identified the critical ESD steps for lesion identification, marking, injection, circumferential cutting, dissection, intraprocedural complication management, and post-procedure examination. We constructed a hierarchical task tree that elaborates the order of tasks in these steps. Furthermore, we developed quantitative ESD performance metrics. We measured task times and scores of 16 ESD surgeries performed by four different endoscopic surgeons. RESULTS: The average time of the marking, injection, and circumferential cutting phases are 203.4 (σ: 205.46), 83.5 (σ: 49.92), 908.4 s. (σ: 584.53), respectively. Cutting the submucosal layer takes most of the time of overall ESD procedure time with an average of 1394.7 s (σ: 908.43). We also performed correlation analysis (Pearson's test) among the performance scores of the tasks. There is a moderate positive correlation (R = 0.528, p = 0.0355) between marking scores and total scores, a strong positive correlation (R = 0.7879, p = 0.0003) between circumferential cutting and submucosal dissection and total scores. Similarly, we noted a strong positive correlation (R = 0.7095, p = 0.0021) between circumferential cutting and submucosal dissection and marking scores. CONCLUSIONS: We elaborated ESD tasks and developed quantitative performance metrics used in analysis of actual surgery performance. These ESD metrics will be used in future validation studies of our VESS simulator.

In Vivo Layer-Specific Mechanical Characterization of Porcine Stomach Tissue Using a Customized Ultrasound Elastography System.

This paper presents in vivo mechanical characterization of the muscularis, submucosa, and mucosa of the porcine stomach wall under large deformation loading. This is particularly important for the development of gastrointestinal pathology-specific surgical intervention techniques. The study is based on testing the cardiac and fundic glandular stomach regions using a custom-developed compression ultrasound elastography system. Particular attention has been paid to elucidate the heterogeneity and anisotropy of tissue response. A Fung hyperelastic material model has been used to model the mechanical response of each tissue layer. A univariate analysis comparing the initial shear moduli of the three layers indicates that the muscularis (5.69 ± 4.06 kPa) is the stiffest followed by the submucosa (3.04 ± 3.32 kPa) and the mucosa (0.56 ± 0.28 kPa). The muscularis is found to be strongly distinguishable from the mucosa tissue in the cardiac and fundic regions based on a multivariate discriminant analysis. The cardiac muscularis is observed to be stiffer than the fundic muscularis tissue (shear moduli of 7.96 ± 3.82 kPa versus 3.42 ± 2.96 kPa), more anisotropic (anisotropic parameter of 2.21 ± 0.77 versus 1.41 ± 0.38), and strongly distinguishable from its fundic counterpart. The results are consistent with the tissue morphology and are in accordance with our previous ex vivo tissue study. Finally, a univariate comparison of the in vivo and ex vivo initial shear moduli for each layer shows that the muscularis and submucosa tissues are softer while in vivo, but the mucosa tissue is stiffer while in vivo. The results concerning the mechanical properties highlight the inhomogeneity and anisotropy of multilayer stomach tissue.

Waveform-Dependent Electrosurgical Effects on Soft Hydrated Tissues.

Electrosurgical procedures are ubiquitously used in surgery. The commonly used power modes, including the coagulation and blend modes, utilize nonsinusoidal or modulated current waveforms. For the same power setting, the coagulation, blend, and pure cutting modes have different heating and thermal damage outcomes due to the frequency dependence of electrical conductivity of soft hydrated tissues. In this paper, we propose a multiphysics model of soft tissues to account for the effects of multifrequency electrosurgical power modes within the framework of a continuum thermomechanical model based on mixture theory. Electrical and frequency spectrum results from different power modes at low- and high-power settings are presented. Model predictions are compared with in vivo electrosurgical heating experiments on porcine liver tissue. The accuracy of the model in predicting experimentally observed temperature profiles is found to be overall greater when frequency-dependence is included. An Arrhenius type model indicates that more tissue damage is correlated with larger duty cycles in multifrequency modes.

A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery.

Multiphysics modeling of evolving topology in the electrosurgical dissection of soft hydrated tissues is a challenging problem, requiring heavy computational resources. In this paper, we propose a hybrid approach that leverages the regressive capabilities of deep convolutional neural networks (CNN) with the precision of conventional solvers to accelerate Multiphysics computations. The electro-thermal problem is solved using a finite element method (FEM) with a Krylov subspace-based iterative solver and a deflation-based block preconditioner. The mechanical deformation induced by evaporation of intra- and extracellular water is obtained using a CNN model. The CNN is trained using a supervised learning framework that maps the nonlinear relationship between the micropore pressure and deformation field for a given tissue topology. The simulation results show that the hybrid approach is significantly more computationally efficient than a FEM-based solution approach using a block-preconditioned Krylov subspace solver and a parametric solution approach using a proper generalized decomposition (PGD) based reduced order model. The accuracy of the hybrid approach is comparable to the ground truth obtained using a standard multiphysics solver. The hydrid approach overcomes the limitations of end-to-end learning including the need for massive datasets for training the network.

Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator.

INTRODUCTION: Research has clearly shown the benefits of surgical simulators to train laparoscopic motor skills required for positive patient outcomes. We have developed the Virtual Basic Laparoscopic Skill Trainer (VBLaST) that simulates tasks from the Fundamentals of Laparoscopic Surgery (FLS) curriculum. This study aims to show convergent validity of the VBLaST pattern cutting module via the CUSUM method to quantify learning curves along with motor skill transfer from simulation environments to ex vivo tissue samples. METHODS: 18 medical students at the University at Buffalo, with no prior laparoscopic surgical skills, were placed into the control, FLS training, or VBLaST training groups. Each training group performed pattern cutting trials for 12 consecutive days on their respective simulation trainers. Following a 2-week break period, the trained students performed three pattern cutting trials on each simulation platform to measure skill retention. All subjects then performed one pattern cutting task on ex vivo cadaveric peritoneal tissue. FLS and VBLaST pattern cutting scores, CUSUM scores, and transfer task completion times were reported. RESULTS: Results indicate that the FLS and VBLaST trained groups have significantly higher task performance scores than the control group in both the VBLaST and FLS environments (p < 0.05). Learning curve results indicate that three out of seven FLS training subjects and four out of six VBLaST training subjects achieved the "senior" performance level. Furthermore, both the FLS and VBLaST trained groups had significantly lower transfer task completion times on ex vivo peritoneal tissue models (p < 0.05). CONCLUSION: We characterized task performance scores for trained VBLaST and FLS subjects via CUSUM analysis of the learning curves and showed evidence that both groups have significant improvements in surgical motor skill. Furthermore, we showed that learned surgical skills in the FLS and VBLaST environments transfer not only to the different simulation environments, but also to ex vivo tissue models.