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.

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.

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.

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.

Towards Real-time Bone Drilling Simulation for Anchor Placement in VR Based Arthroscopic Rotator Cuff Surgery Simulation.

Arthroscopic Rotator Cuff (ARC) is a minimally invasive surgery of the shoulder. ARC training for surgeons is challenging due to confined space, anatomical complexity, requirement of complex hands-eye coordination skills, subjectivity, and low fidelity in existing training mediums. We therefore offer a virtual reality based photorealistic medical simulation, Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST) for objective training. In this study, as a part of ViRCAST, we introduce a virtual reality-based bone drilling simulation. Bone drilling task is one of the most important tasks that surgeons need to perform before anchor placement in ARC. Realistic simulation of bone drilling with force feedback is complex due to real-time mesh modification and simulation constraints. We introduce a GPU based realtime bone drilling simulation for ViRCAST using an adaptive mesh refinement technique. Our GPU based solution improves the drilling simulation realism by enhancing mesh resolution without sacrificing the simulation performance.

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.

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.

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.

Local edge-enhanced active contour for accurate skin lesion border detection.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

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.

Texture based skin lesion abruptness quantification to detect malignancy.

BACKGROUND: Abruptness of pigment patterns at the periphery of a skin lesion is one of the most important dermoscopic features for detection of malignancy. In current clinical setting, abrupt cutoff of a skin lesion determined by an examination of a dermatologist. This process is subjective, nonquantitative, and error-prone. We present an improved computational model to quantitatively measure abruptness of a skin lesion over our previous method. To achieve this, we quantitatively analyze the texture features of a region within the lesion boundary. This region is bounded by an interior border line of the lesion boundary which is determined using level set propagation (LSP) method. This method provides a fast border contraction without a need for extensive boolean operations. Then, we build feature vectors of homogeneity, standard deviation of pixel values, and mean of the pixel values of the region between the contracted border and the original border. These vectors are then classified using neural networks (NN) and SVM classifiers. RESULTS: As lower homogeneity indicates sharp cutoffs, suggesting melanoma, we carried out our experiments on two dermoscopy image datasets, which consist of 800 benign and 200 malignant melanoma cases. LSP method helped produce better results than Kaya et al., 2016 study. By using texture homogeneity at the periphery of a lesion border determined by LSP, as a classification results, we obtained 87% f1-score and 78% specificity; that we obtained better results than in the previous study. We also compared the performances of two different NN classifiers and support vector machine classifier. The best results obtained using combination of RGB color spaces with the fully-connected multi-hidden layer NN. CONCLUSIONS: Computational results also show that skin lesion abrupt cutoff is a reliable indicator of malignancy. Results show that computational model of texture homogeneity along the periphery of skin lesion borders based on LSP is an effective way of quantitatively measuring abrupt cutoff of a lesion.

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.

Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images.

BACKGROUND: Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. METHODS: This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. RESULTS: The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. CONCLUSIONS: The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

Face and content validation of a Virtual Translumenal Endoscopic Surgery Trainer (VTEST™).

BACKGROUND: Natural orifice translumenal endoscopic surgery (NOTES) is an emerging surgical paradigm, where peritoneal access is achieved through one of the natural orifices of the body. It is being reported as a safe and feasible surgical technique with significantly reduced external scarring. Virtual Translumenal Endoscopic Surgical Trainer (VTEST™) is the first virtual reality simulator for the NOTES. The VTEST™ simulator was developed to train surgeons in the hybrid transvaginal NOTES cholecystectomy procedure. The initial version of the VTEST™ simulator underwent face validation at the 2013 Natural Orifice Surgery Consortium for Assessment and Research (NOSCAR) summit. Several areas of improvement were identified as a result, and the corresponding modifications were implemented in the simulator. This manuscript outlines the results of the subsequent evaluation study, performed in order to assess the face and content validity of the latest VTEST™ simulator. METHODS: Twelve subjects participated in an institutional review board-approved study that took place at the 2014 NOSCAR summit. Six of the 12 subjects, who are experts with NOTES experience, were used for face and content validation. The subjects performed the hybrid transvaginal NOTES cholecystectomy procedure on VTEST™ that included identifying the Calot's triangle, clipping and cutting the cystic duct/artery, and detaching the gallbladder. The subjects then answered five-point Likert scale feedback questionnaires for face and content validity. RESULTS: Overall, subjects rated 12/15 questions as 3.0 or greater (60 %), for face validity questions regarding the realism of the anatomical features, interface, and the tasks. Subjects also highly rated the usefulness of the simulator in learning the fundamental NOTES technical skills (3.50 ± 0.84). Content validity results indicate a high level of usefulness of the VTEST™ for training prior to operating room experience (4.17 ± 0.75).

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.

Graphic and haptic simulation for transvaginal cholecystectomy training in NOTES.

BACKGROUND: Natural Orifice Transluminal Endoscopic Surgery (NOTES) provides an emerging surgical technique which usually needs a long learning curve for surgeons. Virtual reality (VR) medical simulators with vision and haptic feedback can usually offer an efficient and cost-effective alternative without risk to the traditional training approaches. Under this motivation, we developed the first virtual reality simulator for transvaginal cholecystectomy in NOTES (VTEST™). METHODS: This VR-based surgical simulator aims to simulate the hybrid NOTES of cholecystectomy. We use a 6DOF haptic device and a tracking sensor to construct the core hardware component of simulator. For software, an innovative approach based on the inner-spheres is presented to deform the organs in real time. To handle the frequent collision between soft tissue and surgical instruments, an adaptive collision detection method based on GPU is designed and implemented. To give a realistic visual performance of gallbladder fat tissue removal by cautery hook, a multi-layer hexahedral model is presented to simulate the electric dissection of fat tissue. RESULTS: From the experimental results, trainees can operate in real time with high degree of stability and fidelity. A preliminary study was also performed to evaluate the realism and the usefulness of this hybrid NOTES simulator. CONCLUSIONS: This prototyped simulation system has been verified by surgeons through a pilot study. Some items of its visual performance and the utility were rated fairly high by the participants during testing. It exhibits the potential to improve the surgical skills of trainee and effectively shorten their learning curve.

A hierarchical task analysis of cricothyroidotomy procedure for a virtual airway skills trainer simulator.

BACKGROUND: Despite the critical importance of cricothyroidotomy (CCT) for patient in extremis, clinical experience with CCT is infrequent, and current training tools are inadequate. The long-term goal is to develop a virtual airway skills trainer that requires a thorough task analysis to determine the critical procedural steps, learning metrics, and parameters for assessment. METHODS: Hierarchical task analysis is performed to describe major tasks and subtasks for CCT. A rubric for performance scoring for each task was derived, and possible operative errors were identified. RESULTS: Time series analyses for 7 CCT videos were performed with 3 different observers. According to Pearson's correlation tests, 3 of the 7 major tasks had a strong correlation between their task times and performance scores. CONCLUSIONS: The task analysis forms the core of a proposed virtual CCT simulator, and highlights links between performance time and accuracy when teaching individual surgical steps of the procedure.