Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligence.

PURPOSE: We have previously developed grading metrics to objectively measure endoscopist performance in endoscopic sleeve gastroplasty (ESG). One of our primary goals is to automate the process of measuring performance. To achieve this goal, the repeated task being performed (grasping or suturing) and the location of the endoscopic suturing device in the stomach (Incisura, Anterior Wall, Greater Curvature, or Posterior Wall) need to be accurately recorded. METHODS: For this study, we populated our dataset using screenshots and video clips from experts carrying out the ESG procedure on ex vivo porcine specimens. Data augmentation was used to enlarge our dataset, and synthetic minority oversampling (SMOTE) to balance it. We performed stomach localization for parts of the stomach and task classification using deep learning for images and computer vision for videos. RESULTS: Classifying the stomach's location from the endoscope without SMOTE for images resulted in 89% and 84% testing and validation accuracy, respectively. For classifying the location of the stomach from the endoscope with SMOTE, the accuracies were 97% and 90% for images, while for videos, the accuracies were 99% and 98% for testing and validation, respectively. For task classification, the accuracies were 97% and 89% for images, while for videos, the accuracies were 100% for both testing and validation, respectively. CONCLUSION: We classified the four different stomach parts manipulated during the ESG procedure with 97% training accuracy and classified two repeated tasks with 99% training accuracy with images. We also classified the four parts of the stomach with a 99% training accuracy and two repeated tasks with a 100% training accuracy with video frames. This work will be essential in automating feedback mechanisms for learners in ESG.

GPU based Position Based Dynamics for Surgical Simulators.

Position Based Dynamics is the most popular approach for simulating dynamic systems in computer graphics. However, volume rendering with linear deformation times is still a challenge in virtual scenes. In this work, we implemented Graphics Processing Unit (GPU)-based Position-Based Dynamics to iMSTK, an open-source toolkit for rapid prototyping interactive multi-modal surgical simulation. We utilized NVIDIA's CUDA toolkit for this implementation and carried out vector calculations on GPU kernels while ensuring that threads do not overwrite the data used in other calculations. We compared our results with an available GPU-based Position-Based Dynamics solver. We gathered results on two computers with different specifications using affordable GPUs. The vertex (959 vertices) and tetrahedral mesh element (2591 elements) counts were kept the same for all calculations. Our implementation was able to speed up physics calculations by nearly 10x. For the size of 128x128, the CPU implementation carried out physics calculations in 7900ms while our implementation carried out the same physics calculations in 820ms.

Evaluation of WebRTC in the Cloud for Surgical Simulations: A case study on Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST).

Web Real-Time Communication (WebRTC) is an open-source technology which enables remote peer-to-peer video and audio connection. It has quickly become the new standard for real-time communications over the web and is commonly used as a video conferencing platform. In this study, we present a different application domain which may greatly benefit from WebRTC technology, that is virtual reality (VR) based surgical simulations. Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST) is our testing platform that we completed preliminary feasibility studies for WebRTC. Since the elasticity of cloud computing provides the ability to meet possible future hardware/software requirements and demand growth, ViRCAST is deployed in a cloud environment. Additionally, in order to have plausible simulations and interactions, any VR-based surgery simulator must have haptic feedback. Therefore, we implemented an interface to WebRTC for integrating haptic devices. We tested ViRCAST on Google cloud through haptic-integrated WebRTC at various client configurations. Our experiments showed that WebRTC with cloud and haptic integrations is a feasible solution for VR-based surgery simulators. From our experiments, the WebRTC integrated simulation produced an average frame rate of 33 fps, and the hardware integration produced an average lag of 0.7 milliseconds in real-time.

Multimodal Approach to Assess a Virtual Reality-based Surgical Training Platform.

Virtual reality (VR) can bring numerous benefits to the learning process. Combining a VR environment with physiological sensors can be beneficial in skill assessment. We aim to investigate trainees' physiological (ECG) and behavioral differences during the virtual reality-based surgical training environment. Our finding showed a significant association between the VR-Score and all participants' total NASA-TLX workload score. The extent of the NASA-TLX workload score was negatively correlated with VR-Score (R2 =0.15, P < 0.03). In time-domain ECG analysis, we found that RMSSD (R2 =0.16, P < 0.05) and pNN50 (R2 =0.15, P < 0.05) scores correlated with significantly higher VR-score of all participants. In this study, we used SVM (linear kernel) and Logistic Regression classification techniques to classify the participants as gamers and non-gamers using data from VR headsets. Both SVM and Logistic Regression accurately classified the participants as gamers and non-gamers with 83% accuracy. For both SVM and Linear Regression, precision was noted as 88%, recall as 83%, and f1-score as 83%. There is increasing interest in characterizing trainees' physiological and behavioral activity profiles in a VR environment, aiming to develop better training and assessment methodologies.

Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos.

Endoscopy is widely employed for diagnostic examination of the interior of organs and body cavities and numerous surgical interventions. Still, the inability to correlate individual 2D images with 3D organ morphology limits its applications, especially in intra-operative planning and navigation, disease physiology, cancer surveillance, etc. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a complete method for the 3D reconstruction of inner organs that suggests image extraction techniques from endoscopic videos and a novel image pre-processing technique to reconstruct and visualize a 3D model of organs from an endoscopic video. We use advanced computer vision methods and do not require any modifications to the clinical-grade endoscopy hardware. We have also formalized an image acquisition protocol through experimentation with a calibrated test bed. We validate the accuracy and robustness of our reconstruction using a test bed with known ground truth. Our method can significantly contribute to endoscopy-based diagnostic and surgical procedures using comprehensive tissue and tumor 3D visualization.

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.

Development and Validation of Task-Specific Metrics for the Assessment of Linear Stapler-Based Small Bowel Anastomosis.

INTRODUCTION: Task-specific metrics facilitate the assessment of surgeon performance. This 3-phased study was designed to (1) develop task-specific metrics for stapled small bowel anastomosis, (2) obtain expert consensus on the appropriateness of the developed metrics, and (3) establish its discriminant validity. METHODS: In Phase I, a hierarchical task analysis was used to develop the metrics. In Phase II, a survey of expert colorectal surgeons established the importance of the developed metrics. In Phase III, to establish discriminant validity, surgical trainees and surgeons, divided into novice and experienced groups, constructed a side-to-side anastomosis on porcine small bowel using a linear cutting stapler. The participants' performances were videotaped and rated by 2 independent observers. Partial least squares regression was used to compute the weights for the task-specific metrics to obtain weighted total score. RESULTS: In Phase II, a total of 45 colorectal surgeons were surveyed: 28 with more than 15 years, 13 with 5 to 15 years, and 4 with less than 5 years of experience. The consensus was obtained on all the task-specific metrics in the more experienced groups. In Phase III, 20 subjects participated equally in both groups. The experienced group performed better than the novice group regardless of the rating scale used: global rating scale (p = 0.009) and the task-specific metrics (p = 0.012). After partial least squares regression, the weighted task-specific metric score continued to show that the experienced group performed better (p < 0.001). CONCLUSION: Task-specific metric items were developed based on expert consensus and showed good discriminant validity compared with a global rating scale between experienced and novice operators. These items can be used for evaluating technical skills in a stapled small bowel anastomosis model.

Scoring metrics for assessing skills in arthroscopic rotator cuff repair: performance comparison study of novice and expert surgeons.

PURPOSE: We aim to develop quantitative performance metrics and a deep learning model to objectively assess surgery skills between the novice and the expert surgeons for arthroscopic rotator cuff surgery. These proposed metrics can be used to give the surgeon an objective and a quantitative self-assessment platform. METHODS: Ten shoulder arthroscopic rotator cuff surgeries were performed by two novices, and fourteen were performed by two expert surgeons. These surgeries were statistically analyzed. Two existing evaluation systems: Basic Arthroscopic Knee Skill Scoring System (BAKSSS) and the Arthroscopic Surgical Skill Evaluation Tool (ASSET), were used to validate our proposed metrics. In addition, a deep learning-based model called Automated Arthroscopic Video Evaluation Tool (AAVET) was developed toward automating quantitative assessments. RESULTS: The results revealed that novice surgeons used surgical tools approximately 10% less effectively and identified and stopped bleeding less swiftly. Our results showed a notable difference in the performance score between the experts and novices, and our metrics successfully identified these at the task level. Moreover, the F1-scores of each class are found as 78%, 87%, and 77% for classifying cases with no-tool, electrocautery, and shaver tool, respectively. CONCLUSION: We have constructed quantitative metrics that identified differences in the performances of expert and novice surgeons. Our ultimate goal is to validate metrics further and incorporate these into our virtual rotator cuff surgery simulator (ViRCAST), which has been under development. The initial results from AAVET show that the capability of the toolbox can be extended to create a fully automated performance evaluation platform.

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.

Digitizing rhinoplasty: a web application with three-dimensional preoperative evaluation to assist rhinoplasty surgeons with surgical planning.

PURPOSE: Rhinoplasty is one of the most common and challenging plastic surgery procedures. The results of the operation have a significant impact on the facial appearance. The planning is critical for successful rhinoplasty surgery. In this paper, we present a web application designed for preoperative rhinoplasty surgery planning. METHODS: The application uses the three-dimensional (3D) model of a patient's face and facilitates marking of an extensive number of facial features and auto-calculation of facial measurements to develop a numerical plan of the surgery. The web application includes definitions, illustrations, and formulas to describe the features and measurements. In addition to the existing measurements, the user can calculate the distance between any two points, the angle between any three points, and the ratio of any two distances. We conducted a survey among experienced rhinoplasty surgeons to get feedback about the web application and to understand their attitude toward utilizing 3D models for preoperative planning. RESULTS: The web application can be accessed and used through any web browser at digitized-rhinoplasty.com. The web application was utilized in our tests and also by the survey participants. The users successfully marked the facial features on the 3D models and reviewed the auto-calculated measurements. The survey results show that the experienced surgeons who tried the web application found it useful for preoperative planning and they also think that utilizing 3D models is beneficial. CONCLUSIONS: The web application introduced in this paper helps analyzing the patient's face in details utilizing 3D models and provides numeric outputs to be used in the rhinoplasty operation planning. The experienced rhinoplasty surgeons that participated to our survey agree that the web app would be a beneficial tool for rhinoplasty surgeons. We aim to further improve the web application with more functionality to help surgeons for preoperative planning of rhinoplasty.

A partition-based optimization model and its performance benchmark for Generative Anatomy Modeling Language.

BACKGROUND: This paper presents a novel iterative approach and rigorous accuracy testing for geometry modeling language - a Partition-based Optimization Model for Generative Anatomy Modeling Language (POM-GAML). POM-GAML is designed to model and create anatomical structures and their variations by satisfying any imposed geometric constraints using a non-linear optimization model. Model partitioning of POM-GAML creates smaller sub-problems of the original model to reduce the exponential execution time required to solve the constraints in linear time with a manageable error. METHOD: We analyzed our model concerning the iterative approach and graph parameters for different constraint hierarchies. The iteration was used to reduce the error for partitions and solve smaller sub-problems generated by various clustering/community detection algorithms. We empirically tested our model with eleven graph parameters. Graphs for each parameter with increasing constraint sets were generated to evaluate the accuracy of our method. RESULTS: The average decrease in normalized error with respect to the original problem using cluster/community detection algorithms for constraint sets was above 63.97%. The highest decrease in normalized error after five iterations for the constraint set of 3900 was 70.31%, while the lowest decrease for the constraint set of 3000 was with 63.97%. Pearson correlation analysis between graph parameters and normalized error was carried out. We identified that graph parameters such as diameter, average eccentricity, global efficiency, and average local efficiency showed strong correlations to the normalized error. CONCLUSIONS: We observed that iteration monotonically decreases the error in all experiments. Our iteration results showed decreased normalized error using the partitioned constrained optimization by linear approximation to the non-linear optimization model.

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.

Systematic approach for content and construct validation: Case studies for arthroscopy and laparoscopy.

BACKGROUND: In minimally invasive surgery, there are several challenges for training novice surgeons, such as limited field-of-view and unintuitive hand-eye coordination due to performing the operation according to video feedback. Virtual reality (VR) surgical simulators are a novel, risk-free, and cost-effective way to train and assess surgeons. METHODS: We developed VR-based simulations to accurately assess and quantify performance of two VR simulations: gentleness simulation for laparoscopy and rotator cuff repair for arthroscopy. We performed content and construct validity studies for the simulators. In our analysis, we systematically rank surgeons using data mining classification techniques. RESULTS: Using classification algorithms such as K-Nearest Neighbors, Support Vector Machines, and Logistic Regression we have achieved near 100% accuracy rate in identifying novices, and up to an 83% accuracy rate identifying experts. Sensitivity and specificity were up to 1.0 and 0.9, respectively. CONCLUSION: Developed methodology to measure and differentiate the highly ranked surgeons and less-skilled surgeons.

Partition-based optimization model for generative anatomy modeling language (POM-GAML).

BACKGROUND: This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities. METHODS: Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints. RESULTS: Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%. CONCLUSION: This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver.

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.

Generative Anatomy Modeling Language (GAML).

BACKGROUND: This paper presents the Generative Anatomy Modeling Language (GAML) for generating variation of 3D virtual human anatomy in real-time. This framework provides a set of operators for modification of a reference base 3D anatomy. The perturbation of the 3D models is satisfied with nonlinear geometry constraints to create an authentic human anatomy. METHODS: GAML was used to create 3D difficult anatomical scenarios for virtual simulation of airway management techniques such as Endotracheal Intubation (ETI) and Cricothyroidotomy (CCT). Difficult scenarios for each technique were defined and the model variations procedurally created with GAML. CONCLUSION: This study presents details of the GAML design, set of operators, types of constraints. Cases of CCT and ETI difficulty were generated and confirmed by expert surgeons. Execution performance pertaining to an increasing complexity of constraints using nonlinear programming was in real-time execution.

A hierarchical task analysis of shoulder arthroscopy for a virtual arthroscopic tear diagnosis and evaluation platform (VATDEP).

BACKGROUND: Shoulder arthroscopy is a minimally invasive surgical procedure for diagnosis and treatment of a shoulder pathology. The procedure is performed with a fiber optic camera, called arthroscope, and instruments inserted through very tiny incisions made around the shoulder. The confined shoulder space, unintuitive camera orientation and constrained instrument motions complicates the procedure. Therefore, surgical competence in arthroscopy entails extensive training especially for psychomotor skills development. Conventional arthroscopy training methods such as mannequins, cadavers or apprenticeship model have limited use attributed to their low-fidelity in realism, cost inefficiency or incurring high risk. However, virtual reality (VR) based surgical simulators offer a realistic, low cost, risk-free training and assessment platform where the trainees can repeatedly perform arthroscopy and receive quantitative feedback on their performances. Therefore, we are developing a VR based shoulder arthroscopy simulation specifically for the rotator cuff ailments that can quantify the surgery performance. Development of such a VR simulation requires a through task analysis that describes the steps and goals of the procedure, comprehensive metrics for quantitative and objective skills and surgical technique assessment. METHODS: We analyzed shoulder arthroscopic rotator cuff surgeries and created a hierarchical task tree. We introduced a novel surgery metrics to reduce the subjectivity of the existing grading metrics and performed video analysis of 14 surgery recordings in the operating room (OR). We also analyzed our video analysis results with respect to the existing proposed metrics in the literature. RESULTS: We used Pearson's correlation tests to find any correlations among the task times, scores and surgery specific information. We determined strong positive correlation between cleaning time vs difficulty in tying suture, cleaning time vs difficulty in passing suture, cleaning time vs scar tissue size, difficulty passing vs difficulty in tying suture, total time and difficulty of the surgery. CONCLUSION: We have established a hierarchical task analysis and analyzed our performance metrics. We will further use our metrics in our VR simulator for quantitative assessment.

Virtual Airway Skills Trainer (VAST) Simulator.

This paper presents a simulation of Virtual Airway Skill Trainer (VAST) tasks. The simulated tasks are a part of two main airway management techniques; Endotracheal Intubation (ETI) and Cricothyroidotomy (CCT). ETI is a simple nonsurgical airway management technique, while CCT is the extreme surgical alternative to secure the airway of a patient. We developed identification of Mallampati class, finding the optimal angle for positioning pharyngeal/mouth axes tasks for ETI and identification of anatomical landmarks and incision tasks for CCT. Both ETI and CCT simulators were used to get physicians' feedback at Society for Education in Anesthesiology and Association for Surgical Education spring meetings. In this preliminary validation study, total 38 participants for ETI and 48 for CCT performed each simulation task and completed pre and post questionnaires. In this work, we present the details of the simulation for the tasks and also the analysis of the collected data from the validation study.

Virtual Intraoperative Cholangiogram Using WebCL.

In this paper, we propose a Virtual Intraoperative Cholangiogram (VIC) training platform. Intraoperative Cholangiogram (IC) is an imaging technique of biliary anatomy with using fluorescent fluids sensitive to the X-Rays. The procedure is often employed to diagnose the difficult cases such as abnormal anatomy or choledocholithiasis during the laparoscopic cholecystectomy. The major challenge in cholangiogram is accurate interpretation of the X-Ray image, which requires extensive case training. However, the training platforms that support generation of various IC cases have been lacking. In this study, we developed a web based platform to generate IC images from any virtual bile duct anatomy. As the generation of X-Ray image from 3D scene is a computationally intensive task, we utilized WebCL technology to parallelize the computation for achieving real-time rates. In this work, we present details of our WebCL IC generation algorithm and benchmark results.