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Significance As trainees practice fundamental surgical skills, they typically rely on performance measures such as time and errors, which are limited in their sensitivity. Aim The goal of our study was to evaluate the use of portable neuroimaging measures to map the neural processes associated with learning basic surgical skills. Approach Twenty-one subjects completed 15 sessions of training on the fundamentals of laparoscopic surgery (FLS) suture with intracorporeal knot-tying task in a box trainer. Functional near infrared spectroscopy data were recorded using an optode montage that covered the prefrontal and sensorimotor brain areas throughout the task. Average oxy-hemoglobin (HbO) changes were determined for repetitions performed during the first week of training compared with the third week of training. Statistical differences between the time periods were evaluated using a general linear model of the HbO changes. Results Average performance scores across task repetitions increased significantly from the first day to the last day of training (p < 0.01). During the first day of training, there was significant lateral prefrontal cortex (PFC) activation. On the final day, significant activation was observed in the PFC, as well as the sensorimotor areas. When comparing the two periods, significant differences in activation (p < 0.05) were found for the right medial PFC and the right inferior parietal gyrus. While gaining proficiency, trainees activated the perception-action cycle to build a perceptual model and then apply the model to improve task execution. Conclusions Learners engaged the sensorimotor areas more substantially as they developed skill on the FLS suturing task. These findings are consistent with findings for the FLS pattern cutting task and contribute to the development of objective metrics for skill evaluation.

This study utilizes Raman spectroscopy to analyze the burn-induced collagen conformational changes in ex vivo porcine skin tissue. Raman spectra of wavenumbers 500–2000 cm−1 were measured for unburnt skin as well as four different burn conditions: (i) 200 °F for 10 s, (ii) 200 °F for the 30 s, (iii) 450 °F for 10 s and (iv) 450 °F for 30 s. The overall spectra reveal that protein and amino acids-related bands have manifested structural changes including the destruction of protein-related functional groups, and transformation from α-helical to disordered structures which are correlated with increasing burn severity. The deconvolution of the amide I region (1580–1720 cm−1) and the analysis of the sub-bands reveal a change of the secondary structure of the collagen from the α-like helix dominated to the β-aggregate dominated one. Such conformational changes may explain the softening of mechanical response in burnt tissues reported in the literature.

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.

The erratum corrects the Acknowledgments section of the published article.

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, F ( 10,1 ) = 7495.5, p

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

Background Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended with no control on the time taken to attain a set level of proficiency, given the trainees’ intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model using information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process. Methods This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees’ learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials. Results We show that the information present in the first 10 trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency ( R 2 = 0.72 ) and the final performance level ( R 2 = 0.89 ). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics. Conclusion Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.

Solids exhibit transverse shrinkage when they are stretched, except auxetics that abnormally demonstrate lateral expansion instead. Graphene possesses the unique normal-auxeticity (NA) transition when it is stretched along the armchair direction but not along the zigzag direction. Here we report on the anisotropic temperature-dependent NA transitions in strained graphene using molecular dynamics simulations. The critical strain where the NA transition occurs increases with respect to an increase in the tilt angle deviating from armchair direction upon uniaxial loading. The magic angle for the NA transition is 10.9°, beyond which the critical strain is close to fracture strain. In addition, the critical strain decreases with an increasing temperature when the tilt angle is smaller than the NA magic angle. Our results shed lights on the unprecedented nonlinear dimensional response of graphene to the large mechanical loading at various temperatures.

There are a large number of materials with mild stiffness, which are not as soft as tissues and not as strong as metals. These semihard materials include energetic materials, molecular crystals, layered materials, and van der Waals crystals. The integrity and mechanical stability are mainly determined by the interactions between instantaneously induced dipoles, the so called London dispersion force or van der Waals force. It is challenging to accurately model the structural and mechanical properties of these semihard materials in the frame of density functional theory where the non-local correlation functionals are not well known. Here, we propose a van der Waals density functional named vdW-DFq to accurately model the density and geometry of semihard materials. Using &beta, cyclotetramethylene tetranitramine as a prototype, we adjust the enhancement factor of the exchange energy functional with generalized gradient approximations. We find this method to be simple and robust over a wide tuning range when calibrating the functional on-demand with experimental data. With a calibrated value q = 1.05 , the proposed vdW-DFq method shows good performance in predicting the geometries of 11 common energetic material molecular crystals and three typical layered van der Waals crystals. This success could be attributed to the similar electronic charge density gradients, suggesting a wide use in modeling semihard materials. This method could be useful in developing non-empirical density functional theories for semihard and soft materials.

A multi-physics model has been developed to investigate the effects of cellular level mechanisms on the electro-thermo-mechanical response of hydrated soft tissues with radiofrequency (RF) activation. A micromechanical model generates an equation of state (EOS) that provides the additional pressure arising from evaporation of intra- and extracellular water as well as temperature to the continuum level thermo-mechanical model. A level set method is used to capture the interfacial evolution of tissue damage with the level set evolution equation derived from the second law of thermodynamics, which is consistent with Griffith’s fracture evolution criterion. The discretized equations are solved simultaneously using a Krylov subspace based iterative solver (GMRES) with block preconditioning that effectively deflates the spectrum of the system matrix, resulting in exponential convergence of the Arnoldi iterations. Example problems, including experimental validation, illustrate the computational accuracy and efficiency of the technique.

Radio-frequency electrosurgical procedures are widely used to simultaneously dissect and coagulate tissue. Experiments suggest that evaporation of cellular and intra-cellular water plays a significant role in the evolution of the temperature field at the tissue level, which is not adequately captured in a single scale energy balance equation. Here, we propose a two-scale model to study the effects of microscale phase change and heat dissipation in response to radiofrequency heating on the tissue level in electrosurgical ablation procedures. At the microscale, the conservation of mass along with thermodynamic and mechanical equilibrium is applied to obtain an equation-of-state (EOS) relating vapor mass fraction to temperature and pressure. The evaporation losses are incorporated in the macro-level energy conservation and results are validated with mean experimental temperature distributions measured from electrosurgical ablation testing on ex vivo porcine liver at different power settings of the electrosurgical instrument. Model prediction of water loss and its effect on the temperature along with the effect of the mechanical properties on results are evaluated and discussed.

BACKGROUND: Mastery of laparoscopic skills is essential in surgical practice and requires considerable time and effort to achieve. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PC(©)) is a virtual simulator that was developed as a computerized version of the pattern cutting task in the Fundamentals of Laparoscopic Surgery (FLS) system. To establish convergent validity for the VBLaST-PC(©), we assessed trainees’ learning curves using the cumulative summation (CUSUM) method and compared them with those on the FLS. METHODS: Twenty-four medical students were randomly assigned to an FLS-training group, a VBLaST-training group, or a control group. Fifteen training sessions, 30 minutes in duration per session per day, were conducted over three weeks. All subjects completed pre-test, post-test, and retention-test (2 weeks after post-test) on both the FLS and VBLaST(©) simulators. Performance data, including time, error, FLS score, learning rate, learning plateau, and CUSUM score, were analyzed. RESULTS: The learning curve for all trained subjects demonstrated increasing performance and a performance plateau. CUSUM analyses showed that five of the seven subjects reached the intermediate proficiency level but none reached the expert proficiency level after 150 practice trials. Performance was significantly improved after simulation training, but only in the assigned simulator. No significant decay of skills after 2 weeks of disuse was observed. Control subjects did not show any learning on the FLS simulator, but improved continually in the VBLaST simulator. CONCLUSIONS: Although VBLaST(©)- and FLS-trained subjects demonstrated similar learning rates and plateaus, the majority of subjects required more than 150 trials to achieve proficiency. Trained subjects demonstrated improved performance in only the assigned simulator, indicating specificity of training. The virtual simulator may provide better opportunities for learning, especially with limited training exposure.

Plenty of new two-dimensional materials including graphyne, graphdiyne, graphone, and graphane have been proposed and unveiled after the discovery of the "wonder material" graphene. Graphyne and graphdiyne are two-dimensional carbon allotropes of graphene with honeycomb structures. Graphone and graphane are hydrogenated derivatives of graphene. The advanced and unique properties of these new materials make them highly promising for applications in next generation nanoelectronics. Here, we briefly review their properties, including structural, mechanical, physical, and chemical properties, as well as their synthesis and applications in nanotechnology. Graphyne is better than graphene in directional electronic properties and charge carriers. With a band gap and magnetism, graphone and graphane show important applications in nanoelectronics and spintronics. Because these materials are close to graphene and will play important roles in carbon-based electronic devices, they deserve further, careful, and thorough studies for nanotechnology applications.

In this paper, we report the development of a technique to characterize layer-specific nonlinear material properties of soft tissue in situ with the potential for in vivo testing. A soft tissue elastography robotic arm system comprising of a robotically manipulated 30 MHz high-resolution ultrasound probe, a custom designed compression head, and load cells has been developed to perform compression ultrasound imaging on the target tissue and measure reaction forces. A multilayer finite element model is iteratively optimized to identify the material coefficients of each layer. Validation has been performed using tissue mimicking agar-based phantoms with a low relative error of ~7% for two-layer phantoms and ~10% error for three layer phantoms when compared to known ground-truth values obtained using a commercial material testing system. The technique has then been used to successfully determine the in situ layer-specific mechanical properties of intact porcine stomach. The mean C 10 and C 20 for a second-order reduced polynomial material model were determined for the muscularis (6.41 ± 0.60, 4.29 ± 1.87 kPa), submucosal (5.21 ± 0.57, 3.68 ± 3.01 kPa), and mucosal layers (0.06 ± 0.02, 0.09 ± 0.24 kPa). Such a system can be utilized to perform in vivo mechanical characterization, which is left as future work.

Bright light emitting monodisperse In2−(x+y)EuxTbyO3 (x = 0.05, 0.1, 0.15, 0.2 and y = 0.05, 0.1, 0.15) nanocrystals were successfully synthesized by a versatile hot-injection colloidal route. The X-ray diffraction and X-ray photoelectron spectroscopy studies clearly showed that Eu and Tb were incorporated into the In2O3 lattice. Surface adsorbed NO3− and Cl− anions of Eu3+ and Tb3+ precursors assist the formation of flower like morphology through the oriented attachment process. Optical absorption spectra have been interpreted in terms of 4f → 5d (Eu, Tb) and 2p(O) → 5d (Eu, Tb) interband transitions. The observation of characteristic emission peaks related to Eu3+ and Tb3+ by indirect excitation suggests that In2O3 is an efficient sensitizer. Proper tuning of the compositions, 15% of Eu and 10% of Tb, gives the most intense white light and excellent chromaticity co-ordinates (0.35, 0.35). The shortening of lifetime with Eu and Tb doping improves the energy transfer from the host In2O3 to dopant Eu3+ and Tb3+ ions.

Indium sulphide (In2S3) quantum dots (QDs) of average size 6 ± 2 nm and hexagonal nanoplatelets of average size 37 ± 4 nm have been synthesized from indium myristate and indium diethyl dithiocarbamate precursors respectively. The absorbance and emission band was tuned with variation of nanocrytal size from very small in the strong confinement regime to very large in the weak confinement regime. The blue emission and its shifting with size has been explained with the donor-acceptor recombination process. The 3d element doping (Mn(2+) and Cu(2+)) is found to be effective for formation of new emission bands at higher wavelengths. The characteristic peaks of Mn(2+) and Cu(2+) and the modification of In(3+) peaks in the x-ray photoelectric spectrum (XPS) confirm the incorporation of Mn(2+) and Cu(2+) into the In2S3 matrix. The simulation of the electron paramagnetic resonance signal indicates the coexistence of isotropic and axial symmetry for In and S vacancies. Moreover, the majority of Mn(2+) ions and sulphur vacancies (VS ) reside on the surface of nanocrystals. The quantum confinement effect leads to an enhancement of band gap up to 3.65 eV in QDs. The formation of Mn 3d levels between conduction band edge and shallow donor states is evidenced from a systematic variation of emission spectra with the excitation wavelength. In2S3 QDs have been established as efficient sensitizers to Mn and Cu emission centers. Fast and slow components of photoluminescence (PL) decay dynamics in Mn and Cu doped QDs are interpreted in terms of surface and bulk recombination processes. Fast and stable photodetctors with high photocurrent gain are fabricated with Mn and Cu doped QDs and are found to be faster than pure In2S3. The fastest response time in Cu doped QDs is an indication of the most suitable system for photodetector devices.

In this paper we introduce a Modified Iterative Constraint Anticipation (MICA) method that provides a unified framework for direct and response-based indirect haptic interaction common in many interactive virtual environments. Collision constraints during response based interaction that are modeled using the linear complementarity problem (LCP) framework resolves collision constraints from response-based interactions while allowing for accurate computation of reaction forces. Direct user manipulation is enabled by the linear projection constraints (LPC). A smoothing filter is used to post-process the reaction forces arising from both LCP and LPC to achieve stable interactions in real-time. The effectiveness of MICA is demonstrated using example problems involving deformable bodies.

Study Objective To validate the Virtual Basic Laparoscopic Skill Trainer (VBLaST-PT; the peg transfer task) for concurrent validity based on its ability to differentiate between novice, intermediate, and expert groups of gynecologists, and the gynecologists' subjective preference between the physical Fundamentals of Laparoscopic Surgery (FLS) system and the virtual reality system. Design Prospective study (Canadian Task Force II-2). Setting Academic medical center. Participants Obstetrics and gynecology residents (n = 18) and attending gynecologists (n = 9). Interventions Twenty-seven subjects were divided into 3 groups: novices (n = 9), intermediates (n = 9), and experts (n = 9). All subjects performed 10 trials of the peg transfer on each simulator. Assessment of laparoscopic performance was based on FLS scoring, whereas a questionnaire was used for subjective evaluation. Measurements and Main Results The performance scores in the 2 simulators were nearly identical. Experts performed better than intermediates and novices in both the FLS trainer and the VBLAST, and intermediates performed better than novices in both simulators. The results also show a significant learning effect on both trainers for all subgroups; however, the greatest learning effect was in the novice group for both trainers. Subjectively, 74% participants preferred the FLS over the VBLaST for training laparoscopic surgical skills. Conclusion This study demonstrates that the peg transfer task was reproduced well in the VBLaST in gynecologic surgeons and trainees. The VBLaST has the potential to be a valuable tool in laparoscopic training for gynecologic surgeons.

International audience; Background Studies on a virtual reality simulator have demonstrated that sleep-deprived residents make more errors. Work-hour restrictions were implemented, among other reasons, to ensure enough sleep time for residents. The objective of this study was to assess the effects of sleep time, perceived fatigue, and experience on surgical performance. We hypothesized that performance would decrease with less sleep and fatigue, and that experienced surgeons would perform better than less experienced surgeons despite sleep deprivation and fatigue. Methods Twenty-two surgical residents and attendings performed a peg transfer task on two simulators: the Fundamentals of Laparoscopic Skills (FLS) trainer and the Virtual Basic Laparoscopic Surgical Trainer (VBLaST©), a virtual version of the FLS. Participants also completed questionnaires to assess their fatigue level and recent sleep hours. Each subject performed 10 trials on each simulator in a counterbalanced order. Performance was measured using the FLS normalized scores, and analyzed using a multiple regression model. Results The multiple regression analysis showed that sleep hours and perceived fatigue were not covariates. No correlation was found between experience level and sleep hours or fatigue. Sleep hours and fatigue did not appear to affect performance. Expertise level was the only significant determinant of performance in both FLS and VBLaST©. Conclusions Restricting resident work-hours was expected to result in less fatigue and better clinical performance. In our study, peg transfer task performance was not affected by sleep hours. Experience level was a significant indicator of performance. Further examination of the complex relationship between sleep hour, fatigue, and clinical performance is needed to support the practice of work-hour restriction for surgical residents.

Mastering laparoscopic surgical skills requires considerable time and effort. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PT(©)) is being developed as a computerized version of the peg transfer task of the Fundamentals of Laparoscopic Surgery (FLS) system using virtual reality technology. We assessed the learning curve of trainees on the VBLaST-PT(©) using the cumulative summation (CUSUM) method and compared them with those on the FLS to establish convergent validity for the VBLaST-PT(©).Eighteen medical students from were assigned randomly to one of three groups: control, VBLaST-training, and FLS-training. The VBLaST and the FLS groups performed a total of 150 trials of the peg-transfer task over a 3-week period, 5 days a week. Their CUSUM scores were computed based on predefined performance criteria (junior, intermediate, and senior levels).Of the six subjects in the VBLaST-training group, five achieved at least the "junior" level, three achieved the "intermediate" level, and one achieved the "senior" level of performance criterion by the end of the 150 trials. In comparison, for the FLS group, three students achieved the "senior" criterion and all six students achieved the "intermediate" and "junior" criteria by the 150th trials. Both the VBLaST-PT(©) and the FLS systems showed significant skill improvement and retention, albeit with system specificity as measured by transfer of learning in the retention test: The VBLaST-trained group performed better on the VBLaST-PT(©) than on FLS (p = 0.003), whereas the FLS-trained group performed better on the FLS than on VBLaST-PT(©) (p = 0.002).We characterized the learning curve for a virtual peg transfer task on the VBLaST-PT(©) and compared it with the FLS using CUSUM analysis. Subjects in both training groups showed significant improvement in skill performance, but the transfer of training between systems was not significant.

INTRODUCTION AND STUDY AIM: Natural orifice translumenal endoscopic surgery (NOTES) is an emerging surgical technique that requires a cautious adoption approach to ensure patient safety. High-fidelity virtual-reality-based simulators allow development of new surgical procedures and tools and train medical personnel without risk to human patients. As part of a project funded by the National Institutes of Health, we are developing the virtual transluminal endoscopic surgery trainer (VTEST) for this purpose. The objective of this study is to conduct a structured needs analysis to identify the design parameters for such a virtual-reality-based simulator for NOTES.A 30-point questionnaire was distributed at the 2011 National Orifice Surgery Consortium for Assessment and Research meeting to obtain responses from experts. Ordinal logistic regression and the Wilcoxon rank-sum test were used for analysis.A total of 22 NOTES experts participated in the study. Cholecystectomy (CE, 68 %) followed by appendectomy (AE, 63 %) (CE vs AE, p = 0.0521) was selected as the first choice for simulation. Flexible (FL, 47 %) and hybrid (HY, 47 %) approaches were equally favorable compared with rigid (RI, 6 %) with p0.001 for both FL versus RI and HY versus RI. The transvaginal approach was preferred 3 to 1 to the transgastric. Most participants preferred two-channel (2C) scopes (65 %) compared with single (1C) or three (3C) or more channels with p0.001 for both 2C versus 1C and 2C versus 3C. The importance of force feedback and the utility of a virtual NOTES simulator in training and testing new tools for NOTES were rated very high by the participants.Our study reinforces the importance of developing a virtual NOTES simulator and clearly presents expert preferences. The results of this analysis will direct our initial development of the VTEST platform.

Background The FLS training tool box has now been adopted by the Society of Gastrointestinal Endoscopic Surgeons (SAGES) as an official training tool for minimally invasive procedures. Methods To overcome the limitations of the physical FLS training tool box, we have developed a Virtual Basic Laparoscopic Skill Trainer (VBLaSTTM) system, which is a 3D simulator that will allow trainees to acquire basic laparoscopic skill. Results The outcome of this work is the development of an integrated visio-haptic workstation environment including force feedback devices and a stereo display interface whereby trainees can practice on virtual versions of the FLS. Realistic graphical rendering and high fidelity haptic interactions are achieved. Conclusions Surgical skill training is a long and tedious process of acquiring fine motor skills. It is expected that residents would start on trainers such as VBLaSTTM and after reaching a certain level of competence would progress to the more complex trainers for training on specific surgical procedures. Copyright © 2008 John Wiley & Sons, Ltd.