Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8-10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13-30 Hz, p = 0.049), beta 1 (15-18 Hz, p = 0.014), and beta 2 (19-22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME CORE COMPETENCIES: Practice-Based Learning and Improvement.

Stain transfer using Generative Adversarial Networks and disentangled features.

With the digitization of histopathology, machine learning algorithms have been developed to help pathologists. Color variation in histopathology images degrades the performance of these algorithms. Many models have been proposed to resolve the impact of color variation and transfer histopathology images to a single stain style. Major shortcomings include manual feature extraction, bias on a reference image, being limited to one style to one style transfer, dependence on style labels for source and target domains, and information loss. We propose two models, considering these shortcomings. Our main novelty is using Generative Adversarial Networks (GANs) along with feature disentanglement. The models extract color-related and structural features with neural networks; thus, features are not hand-crafted. Extracting features helps our models do many-to-one stain transformations and require only target-style labels. Our models also do not require a reference image by exploiting GAN. Our first model has one network per stain style transformation, while the second model uses only one network for many-to-many stain style transformations. We compare our models with six state-of-the-art models on the Mitosis-Atypia Dataset. Both proposed models achieved good results, but our second model outperforms other models based on the Histogram Intersection Score (HIS). Our proposed models were applied to three datasets to test their performance. The efficacy of our models was also evaluated on a classification task. Our second model obtained the best results in all the experiments with HIS of 0.88, 0.85, 0.75 for L-channel, a-channel, and b-channel, using the Mitosis-Atypia Dataset and accuracy of 90.3% for classification.

Dietary nutrients' intake and sleep quality in peritoneal dialysis patients.

Sleep disturbances are common in dialysis patients. However, there is a lack of information on nutritional determinants of sleep disorders in dialysis patients. The objective of the current study was to investigate the association between nutrients' intake and sleep quality in peritoneal dialysis patients. The cross-sectional study was done on 114 peritoneal dialysis patients referred to Alzahra and Khorshid hospitals, Isfahan, Iran. Information on sleep quality and dietary intakes were collected using Pittsburgh sleep quality index and 168-item food frequency questionnaire respectively. Anthropometric measurements were done by a trained dietitian based on standard protocols. Socio-demographic and clinical data were obtained through a structured questionnaire. The binary logistic regression model was used to detect the association between nutrients' intake and sleep quality. Our results indicated that there was not any significant difference in basic (socio-demographic and clinical) characteristics between peritoneal dialysis patients with good and poor sleep quality (p>0.05). The results of logistic regression indicated a positive significant association between dietary intake of carbohydrate (OR:3;95% CI:1.32-6.81; p<0.05), fat (OR:3;95% CI:1.32-6.81; p<0.05), and fiber (OR:2.53;95% CI: 1.12-5.67; p<0.05) with poor sleep quality in crude and adjusted models (p<0.05). However, there was not any significant association between dietary intake of protein and poor sleep quality (p>0.05). The results of the present study indicated that dietary intake of nutrients affect sleep quality in dialysis patients. These results help healthcare professionals in making nutritional interventions to improve sleep quality in dialysis patients.

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task.

This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Originally, 150 features were extracted followed by statistical and forward feature selection. The selected features were provided to 4 classifiers, namely, K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Sets of 5 to 30 selected features were provided to the classifiers. A working point of 15 premium features resulted in accuracy values as high as 90% using the Supprt Vector Machine. The obtained results highlight the potentials of machine learning, applied to VR simulation data, to help realign the traditional apprenticeship educational paradigm to a more objective model, based on proven performance standards. Graphical abstract Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level.

Brain activity during time to contact estimation: an EEG study.

Understanding the neural mechanisms associated with time to contact (TTC) estimation is an intriguing but challenging task. Despite the importance of TTC estimation in our everyday life, few studies have been conducted on it, and there are still a lot of unanswered questions and unknown aspects of this issue. In this study, we intended to address one of these unknown aspects. We used independent component analysis to systematically assess EEG substrates associated with TTC estimation using two experiments: (1) transversal motion experiment (when a moving object passes transversally in the frontoparallel plane from side to side in front of the observer), and (2) head-on motion experiment (when the observer is on the motion path of the moving object). We also studied the energy of all EEG sources in these two experiments. The results showed that brain regions involved in the transversal and head-on motion experiments were the same. However, the energy used by some brain regions in the head-on motion experiment, including some regions in left parietotemporal and left frontal lobes, was significantly higher than the energy used by those regions in the transversal motion experiment. These brain regions are dominantly associated with different kinds of visual attention, integration of visual information, and responding to visual motion.

Tremor Assessment during Virtual Reality Brain Tumor Resection.

OBJECTIVE: Assessment of physiological tremor during neurosurgical procedures may provide further insights into the composites of surgical expertise. Virtual reality platforms may provide a mechanism for the quantitative assessment of physiological tremor. In this study, a virtual reality simulator providing haptic feedback was used to study physiological tremor in a simulated tumor resection task with participants from a "skilled" group and a "novice" group. DESIGN: The task involved using a virtual ultrasonic aspirator to remove a series of virtual brain tumors with different visual and tactile characteristics without causing injury to surrounding tissue. Power spectral density analysis was employed to quantitate hand tremor during tumor resection. Statistical t test was used to determine tremor differences between the skilled and novice groups obtained from the instrument tip x, y, z coordinates, the instrument roll, pitch, yaw angles, and the instrument haptic force applied during tumor resection. SETTING: The study was conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. PARTICIPANTS: The skilled group comprised 23 neurosurgeons and senior residents and the novice group comprised 92 junior residents and medical students. RESULTS: The spectral analysis allowed quantitation of physiological tremor during virtual reality tumor resection. The skilled group displayed smaller physiological tremor than the novice group in all cases. In 3 out of 7 cases the difference was statistically significant. CONCLUSIONS: The first investigation of the application of a virtual reality platform is presented for the quantitation of physiological tremor during a virtual reality tumor resection task. The goal of introducing such methodology to assess tremor is to highlight its potential educational application in neurosurgical resident training and in helping to further define the psychomotor skill set of surgeons.

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation.

Importance: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms. Objective: To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure. Design, Setting, and Participants: Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections. Exposures: All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times. Main Outcomes and Measures: Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership. Results: A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified. Conclusions and Relevance: In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery.

Drift-diffusion explains response variability and capacity for tracking objects.

Being able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving. We showed that the pattern of subject responses had an error variance best explained by an inverse Gaussian distribution and consistent with the output of a biased drift-diffusion model. Furthermore, we demonstrated that the pattern of errors made when tracking two objects showed a level of dependence that was consistent with subjects using a single decision variable for reporting the TTC for two objects. This finding reveals a serious limitation in the capacity for tracking multiple objects resulting in error propagation between objects. Apart from explaining our own data, our approach helps interpret previous findings such as asymmetric interference when tracking multiple objects.

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.

OBJECTIVE: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. DESIGN: The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. SETTING: This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. PARTICIPANTS: The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. RESULTS: Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. CONCLUSIONS: This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.

Spiral waves in externally excited neuronal network: Solvable model with a monotonically differentiable magnetic flux.

Spiral waves are particular spatiotemporal patterns connected to specific phase singularities representing topological wave dislocations or nodes of zero amplitude, witnessed in a wide range of complex systems such as neuronal networks. The appearance of these waves is linked to the network structure as well as the diffusion dynamics of its blocks. We report a novel form of the Hindmarsh-Rose neuron model utilized as a square neuronal network, showing the remarkable multistructure of dynamical patterns ranging from characteristic spiral wave domains of spatiotemporal phase coherence to regions of hyperchaos. The proposed model comprises a hyperbolic memductance function as the monotone differentiable magnetic flux. Hindmarsh-Rose neurons with an external electromagnetic excitation are considered in three different cases: no excitation, periodic excitation, and quasiperiodic excitation. We performed an extensive study of the neuronal dynamics including calculation of equilibrium points, bifurcation analysis, and Lyapunov spectrum. We have found the property of antimonotonicity in bifurcation scenarios with no excitation or periodic excitation and identified wide regions of hyperchaos in the case of quasiperiodic excitation. Furthermore, the formation and elimination of the spiral waves in each case of external excitation with respect to stimuli parameters are investigated. We have identified novel forms of Hindmarsh-Rose bursting dynamics. Our findings reveal multipartite spiral wave formations and symmetry breaking spatiotemporal dynamics of the neuronal model that may find broad practical applications.

Intravascular optical coherence tomography to validate finite-element simulation of angioplasty balloon inflation.

Concrete methods are lacking to examine angioplasty simulation results. For the first time, we explored the application of intravascular optical coherence tomography (IVOCT) to experimentally validate results obtained from finite-element simulation of angioplasty balloon deployment. In order to simulate each experimental scenario, IVOCT images were used to create initial geometrical models for the balloon and the phantoms. The study comprised three scenarios. The first scenario involved experimentally monitoring as well as simulating free expansion of the balloon. The second scenario involved experimentally monitoring as well as simulating balloon inflation inside three artery phantoms with different mechanical properties. The third scenario involved experimentally monitoring as well as simulating balloon unfolding and inflation inside a multilayer phantom. Using the first scenario, a constitutive model was developed for the balloon and was tuned to fit the IVOCT balloon inflation monitoring results. This model was used to simulate the balloon's response in simulations involving phantoms corresponding to the second and third scenarios. Diameter values were calculated both from images and simulation results. These values were then compared for each scenario. The obtained results highlight the potentials of IVOCT monitoring to validate simulation procedures.

Development of a performance model for virtual reality tumor resections.

OBJECTIVE: Previous work from the authors has shown that hand ergonomics plays an important role in surgical psychomotor performance during virtual reality brain tumor resections. In the current study they propose a hypothetical model that integrates the human and task factors at play during simulated brain tumor resections to better understand the hand ergonomics needed for optimal safety and efficiency. They hypothesize that 1) experts (neurosurgeons), compared to novices (residents and medical students), spend a greater proportion of their time in direct contact with critical tumor areas; 2) hand ergonomic conditions (most favorable to unfavorable) prompt participants to adapt in order to optimize tumor resection; and 3) hand ergonomic adaptation is acquired with increasing expertise. METHODS: In an earlier study, experts (neurosurgeons) and novices (residents and medical students) were instructed to resect simulated brain tumors on the NeuroVR (formerly NeuroTouch) virtual reality neurosurgical simulation platform. For the present study, the simulated tumors were divided into four quadrants (Q1 to Q4) to assess hand ergonomics at various levels of difficulty. The spatial distribution of time expended, force applied, and tumor volume removed was analyzed for each participant group (total of 22 participants). RESULTS: Neurosurgeons spent a significantly greater percentage of their time in direct contact with critical tumor areas. Under the favorable hand ergonomic conditions of Q1 and Q3, neurosurgeons and senior residents spent significantly more time in Q1 than in Q3. Although forces applied in these quadrants were similar, neurosurgeons, having spent more time in Q1, removed significantly more tumor in Q1 than in Q3. In a comparison of the most favorable (Q2) to unfavorable (Q4) hand ergonomic conditions, neurosurgeons adapted the forces applied in each quadrant to resect similar tumor volumes. Differences between Q2 and Q4 were emphasized in measures of force applied per second, tumor volume removed per second, and tumor volume removed per unit of force applied. In contrast, the hand ergonomics of medical students did not vary across quadrants, indicating the existence of an "adaptive capacity" in neurosurgeons. CONCLUSIONS: The study results confirm the experts' (neurosurgeons) greater capacity to adapt their hand ergonomics during simulated neurosurgical tasks. The proposed hypothetical model integrates the study findings with various human and task factors that highlight the importance of learning in the acquisition of hand ergonomic adaptation.

Virtual Reality Tumor Resection: The Force Pyramid Approach.

BACKGROUND: The force pyramid is a novel visual representation allowing spatial delineation of instrument force application during surgical procedures. In this study, the force pyramid concept is employed to create and quantify dominant hand, nondominant hand, and bimanual force pyramids during resection of virtual reality brain tumors. OBJECTIVE: To address 4 questions: Do ergonomics and handedness influence force pyramid structure? What are the differences between dominant and nondominant force pyramids? What is the spatial distribution of forces applied in specific tumor quadrants? What differentiates "expert" and "novice" groups regarding their force pyramids? METHODS: Using a simulated aspirator in the dominant hand and a simulated sucker in the nondominant hand, 6 neurosurgeons and 14 residents resected 8 different tumors using the CAE NeuroVR virtual reality neurosurgical simulation platform (CAE Healthcare, Montréal, Québec and the National Research Council Canada, Boucherville, Québec). Position and force data were used to create force pyramids and quantify tumor quadrant force distribution. RESULTS: Force distribution quantification demonstrates the critical role that handedness and ergonomics play on psychomotor performance during simulated brain tumor resections. Neurosurgeons concentrate their dominant hand forces in a defined crescent in the lower right tumor quadrant. Nondominant force pyramids showed a central peak force application in all groups. Bimanual force pyramids outlined the combined impact of each hand. Distinct force pyramid patterns were seen when tumor stiffness, border complexity, and color were altered. CONCLUSION: Force pyramids allow delineation of specific tumor regions requiring greater psychomotor ability to resect. This information can focus and improve resident technical skills training.

Automaticity of Force Application During Simulated Brain Tumor Resection: Testing the Fitts and Posner Model.

OBJECTIVE: The Fitts and Posner model of motor learning hypothesized that with deliberate practice, learners progress through stages to an autonomous phase of motor ability. To test this model, we assessed the automaticity of neurosurgeons, senior residents, and junior residents when operating on 2 identical tumors using the NeuroVR virtual reality simulation platform. DESIGN: Participants resected 9 identical simulated tumors on 2 occasions (total = 18 resections). These resections were separated by the removal of a variable number of tumors with different visual and haptic complexities to mirror neurosurgical practice. Consistency of force application was used as a metric to assess automaticity and was defined as applying forces 1 standard deviation above or below a specific mean force application. Amount and specific location of force application during second identical tumor resection was compared to that used for the initial tumor. SETTING: This study was conducted at the McGill Neurosurgical Simulation Research and Training Center, Montreal Neurologic Institute and Hospital, Montreal, Canada. PARTICIPANTS: Nine neurosurgeons, 10 senior residents, and 8 junior residents. RESULTS: Neurosurgeons display statistically significant increased consistency of force application when compared to resident groups when results from all tumor resections were assessed. Assessing individual tumor types demonstrates significant differences between the neurosurgeon and resident groups when resecting hard stiffness similar-to-background (white) tumors and medium-stiffness tumors. No statistical difference in consistency of force application was found when junior and senior residents were compared. CONCLUSION: "Experts" display significantly more automaticity when operating on identical simulated tumors separated by a series of different tumors using the NeuroVR platform. These results support the Fitts and Posner model of motor learning and are consistent with the concept that automaticity improves after completing residency training. The potential educational application of our findings is outlined related to neurosurgical resident training.

Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task.

OBJECTIVE Severe bleeding during neurosurgical operations can result in acute stress affecting the bimanual psychomotor performance of the operator, leading to surgical error and an adverse patient outcome. Objective methods to assess the influence of acute stress on neurosurgical bimanual psychomotor performance have not been developed. Virtual reality simulators, such as NeuroTouch, allow the testing of acute stress on psychomotor performance in risk-free environments. Thus, the purpose of this study was to explore the impact of a simulated stressful virtual reality tumor resection scenario by utilizing NeuroTouch to answer 2 questions: 1) What is the impact of acute stress on bimanual psychomotor performance during the resection of simulated tumors? 2) Does acute stress influence bimanual psychomotor performance immediately following the stressful episode? METHODS Study participants included 6 neurosurgeons, 6 senior and 6 junior neurosurgical residents, and 6 medical students. Participants resected a total of 6 simulated tumors, 1 of which (Tumor 4) involved uncontrollable "intraoperative" bleeding resulting in simulated cardiac arrest and thus providing the acute stress scenario. Tier 1 metrics included extent of blood loss, percentage of tumor resected, and "normal" brain tissue volume removed. Tier 2 metrics included simulated suction device (sucker) and ultrasonic aspirator total tip path length, as well as the sum and maximum forces applied in using these instruments. Advanced Tier 2 metrics included efficiency index, coordination index, ultrasonic aspirator path length index, and ultrasonic aspirator bimanual forces ratio. All metrics were assessed before, during, and after the stressful scenario. RESULTS The stress scenario caused expected significant increases in blood loss in all participant groups. Extent of tumor resected and brain volume removed decreased in the junior resident and medical student groups. Sucker total tip path length increased in the neurosurgeon group, whereas sucker forces increased in the senior resident group. Psychomotor performance on advanced Tier 2 metrics was altered during the stress scenario in all participant groups. Performance on all advanced Tier 2 metrics returned to pre-stress levels in the post-stress scenario tumor resections. CONCLUSIONS Results demonstrated that acute stress initiated by simulated severe intraoperative bleeding significantly decreases bimanual psychomotor performance during the acute stressful episode. The simulated intraoperative bleeding event had no significant influence on the advanced Tier 2 metrics monitored during the immediate post-stress operative performance.

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection.

OBJECTIVE Virtual reality simulators allow development of novel methods to analyze neurosurgical performance. The concept of a force pyramid is introduced as a Tier 3 metric with the ability to provide visual and spatial analysis of 3D force application by any instrument used during simulated tumor resection. This study was designed to answer 3 questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on the visual and haptic characteristics of simulated tumors? METHODS Using a virtual reality simulator, NeuroVR (formerly NeuroTouch), ultrasonic aspirator force application was continually assessed during resection of simulated brain tumors by neurosurgeons, residents, and medical students. The participants performed simulated resections of 18 simulated brain tumors with different visual and haptic characteristics. The raw data, namely, coordinates of the instrument tip as well as contact force values, were collected by the simulator. To provide a visual and qualitative spatial analysis of forces, the authors created a graph, called a force pyramid, representing force sum along the z-coordinate for different xy coordinates of the tool tip. RESULTS Sixteen neurosurgeons, 15 residents, and 84 medical students participated in the study. Neurosurgeon, resident and medical student groups displayed easily distinguishable 3D "force pyramid fingerprints." Neurosurgeons had the lowest force pyramids, indicating application of the lowest forces, followed by resident and medical student groups. Handedness, ergonomics, and visual and haptic tumor characteristics resulted in distinct well-defined 3D force pyramid patterns. CONCLUSIONS Force pyramid fingerprints provide 3D spatial assessment displays of instrument force application during simulated tumor resection. Neurosurgeon force utilization and ergonomic data form a basis for understanding and modulating resident force application and improving patient safety during tumor resection.

Bimanual Psychomotor Performance in Neurosurgical Resident Applicants Assessed Using NeuroTouch, a Virtual Reality Simulator.

OBJECTIVE: Current selection methods for neurosurgical residents fail to include objective measurements of bimanual psychomotor performance. Advancements in computer-based simulation provide opportunities to assess cognitive and psychomotor skills in surgically naive populations during complex simulated neurosurgical tasks in risk-free environments. This pilot study was designed to answer 3 questions: (1) What are the differences in bimanual psychomotor performance among neurosurgical residency applicants using NeuroTouch? (2) Are there exceptionally skilled medical students in the applicant cohort? and (3) Is there an influence of previous surgical exposure on surgical performance? DESIGN: Participants were instructed to remove 3 simulated brain tumors with identical visual appearance, stiffness, and random bleeding points. Validated tier 1, tier 2, and advanced tier 2 metrics were used to assess bimanual psychomotor performance. Demographic data included weeks of neurosurgical elective and prior operative exposure. SETTING: This pilot study was carried out at the McGill Neurosurgical Simulation Research and Training Center immediately following neurosurgical residency interviews at McGill University, Montreal, Canada. PARTICIPANTS: All 17 medical students interviewed were asked to participate, of which 16 agreed. RESULTS: Performances were clustered in definable top, middle, and bottom groups with significant differences for all metrics. Increased time spent playing music, increased applicant self-evaluated technical skills, high self-ratings of confidence, and increased skin closures statistically influenced performance on univariate analysis. A trend for both self-rated increased operating room confidence and increased weeks of neurosurgical exposure to increased blood loss was seen in multivariate analysis. CONCLUSIONS: Simulation technology identifies neurosurgical residency applicants with differing levels of technical ability. These results provide information for studies being developed for longitudinal studies on the acquisition, development, and maintenance of psychomotor skills. Technical abilities customized training programs that maximize individual resident bimanual psychomotor training dependant on continuously updated and validated metrics from virtual reality simulation studies should be explored.

Neurosurgical Assessment of Metrics Including Judgment and Dexterity Using the Virtual Reality Simulator NeuroTouch (NAJD Metrics).

Advances in computer-based technology has created a significant opportunity for implementing new training paradigms in neurosurgery focused on improving skill acquisition, enhancing procedural outcome, and surgical skills assessment. NeuroTouch is a computer-based virtual reality system that can generate output data known as metrics from operator performance during simulated brain tumor resection. These measures of quantitative assessment are used to track and compare psychomotor performance during simulated operative procedures. Data output from the NeuroTouch system is recorded in a comma-separated values file. Data mining from this file and subsequent metrics development requires the use of sophisticated software and engineering expertise. In this article, we introduce a system to extract a series of new metrics using the same data file using Excel software. Based on the data contained in the NeuroTouch comma-separated values file, 13 novel NeuroTouch metrics were developed and classified. Tier 1 metrics include blood loss, tumor percentage resected, and total simulated normal brain volume removed. Tier 2 metrics include total instrument tip path length, maximum force applied, sum of forces utilized, and average forces utilized by the simulated ultrasonic aspirator and suction instrument along with pedal activation frequency of the ultrasonic aspirator. Advanced tier 2 metrics include instrument tips average separation distance, efficiency index, ultrasonic aspirator path length index, coordination index, and ultrasonic aspirator bimanual forces ratio. This system of data extraction provides researchers expedited access for analyzing the data files available for NeuroTouch platform to assess the multiple psychomotor and cognitive neurosurgical skills involved in complex surgical procedures.

Prevention of postoperative nausea and vomiting with a subhypnotic dose of Propofol in patients undergoing lower abdominal surgery: A prospective, randomized, double-blind study.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a common complication after general anesthesia in patients undergoing elective lower abdominal surgery. We aimed to compare the effect of a sub hypnotic dose of Propofol in the prevention of PONV after lower abdominal surgery with that of the conventional antiemetic drug Metoclopramide. MATERIALS AND METHODS: In this prospective, randomized, double-blind, placebo-controlled study, 104 patients with American Society of Anesthesiologists (ASA) class I or II status, aged 18-65 years, and undergoing elective lower abdominal surgery were randomized to one of four groups (n = 26 each). The patients in the four groups were administered intravenously Propofol 20 mg (G1), Propofol 30 mg (G2), Metoclopramide 10 mg (G3), and placebo (G4), 15 min before skin closure. All episodes of PONV during the first 24 h after anesthesia were recorded by an investigator who was blinded to treatment assignment. RESULTS: There were no significant differences between the treatment groups with regard to their gender, age, ASA class, duration of surgery, duration of recovery time and hospital stay, and also body mass index (BMI) (P > 0.05). The prevalence of PONV 0-6 h after anesthesia was 23.08% with Propofol 20 mg (P = 0.005), 15.38% with Propofol 30 mg (P = 0.016), 15.38% with Metoclopramide 10 mg (P = 0.016), compared to 30.77% with placebo (P = 0.005). CONCLUSIONS: Administration of a subhypnotic dose of Propofol (30 mg) was found to be as effective as 10 mg Metoclopramide in reducing the incidence and severity of PONV in adult patients undergoing elective lower abdominal surgeries under Isoflurane-based anesthesia in the early postoperative period.

Proficiency performance benchmarks for removal of simulated brain tumors using a virtual reality simulator NeuroTouch.

OBJECTIVE: Assessment of neurosurgical technical skills involved in the resection of cerebral tumors in operative environments is complex. Educators emphasize the need to develop and use objective and meaningful assessment tools that are reliable and valid for assessing trainees' progress in acquiring surgical skills. The purpose of this study was to develop proficiency performance benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills performance during simulated brain tumor resection using a new virtual reality simulator (NeuroTouch). DESIGN: Each participant performed the resection of 18 simulated brain tumors of different complexity using the NeuroTouch platform. Surgical performance was computed using Tier 1 and Tier 2 metrics derived from NeuroTouch simulator data consisting of (1) safety metrics, including (a) volume of surrounding simulated normal brain tissue removed, (b) sum of forces utilized, and (c) maximum force applied during tumor resection; (2) quality of operation metric, which involved the percentage of tumor removed; and (3) efficiency metrics, including (a) instrument total tip path lengths and (b) frequency of pedal activation. SETTING: All studies were conducted in the Neurosurgical Simulation Research Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. PARTICIPANTS: A total of 33 participants were recruited, including 17 experts (board-certified neurosurgeons) and 16 novices (7 senior and 9 junior neurosurgery residents). RESULTS: The results demonstrated that "expert" neurosurgeons resected less surrounding simulated normal brain tissue and less tumor tissue than residents. These data are consistent with the concept that "experts" focused more on safety of the surgical procedure compared with novices. By analyzing experts' neurosurgical technical skills performance on these different metrics, we were able to establish benchmarks for goal proficiency performance training of neurosurgery residents. CONCLUSION: This study furthers our understanding of expert neurosurgical performance during the resection of simulated virtual reality tumors and provides neurosurgical trainees with predefined proficiency performance benchmarks designed to maximize the learning of specific surgical technical skills.