Neurosurgical virtual reality simulation metrics to assess psychomotor skills during brain tumor resection

Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: A multilayered approach using transfer learning and connection weights analysis

BACKGROUND

Virtual and augmented reality surgical simulators, integrated with machine learning, are becoming essential for training psychomotor skills, and analyzing surgical performance. Despite the promise of methods like the Connection Weights Algorithm, the small sample sizes (small number of participants (N)) typical of these trials challenge the generalizability and robustness of models. Approaches like data augmentation and transfer learning from models trained on similar surgical tasks address these limitations.

OBJECTIVE

To demonstrate the efficacy of artificial neural network and transfer learning algorithms in evaluating virtual surgical performances, applied to a simulated oblique lateral lumbar interbody fusion technique in an augmented and virtual reality simulator.

DESIGN

The study developed and integrated artificial neural network algorithms within a novel simulator platform, using data from the simulated tasks to generate 276 performance metrics across motion, safety, and efficiency. Innovatively, it applies transfer learning from a pre-trained ANN model developed for a similar spinal simulator, enhancing the training process, and addressing the challenge of small datasets.

SETTING

Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada.

PARTICIPANTS

Twenty-seven participants divided into 3 groups: 9 post-residents, 6 senior and 12 junior residents.

RESULTS

Two models, a stand-alone model trained from scratch and another leveraging transfer learning, were trained on nine selected surgical metrics achieving 75 % and 87.5 % testing accuracy respectively.

CONCLUSIONS

This study presents a novel blueprint for addressing limited datasets in surgical simulations through the strategic use of transfer learning and data augmentation. It also evaluates and reinforces the application of the Connection Weights Algorithm from our previous publication. Together, these methodologies not only enhance the precision of performance classification but also advance the validation of surgical training platforms.

State of the Art in Immersive Interactive Technologies for Surgery Simulation: A Review and Prospective

Immersive technologies have thrived on a strong foundation of software and hardware, injecting vitality into medical training. This surge has witnessed numerous endeavors incorporating immersive technologies into surgery simulation for surgical skills training, with a growing number of researchers delving into this domain. Relevant experiences and patterns need to be summarized urgently to enable researchers to establish a comprehensive understanding of this field, thus promoting its continuous growth. This study provides a forward-looking perspective by reviewing the latest development of immersive interactive technologies for surgery simulation. The investigation commences from a technological standpoint, delving into the core aspects of virtual reality (VR), augmented reality (AR) and mixed reality (MR) technologies, namely, haptic rendering and tracking. Subsequently, we summarize recent work based on the categorization of minimally invasive surgery (MIS) and open surgery simulations. Finally, the study showcases the impressive performance and expansive potential of immersive technologies in surgical simulation while also discussing the current limitations. We find that the design of interaction and the choice of immersive technology in virtual surgery development should be closely related to the corresponding interactive operations in the real surgical speciality. This alignment facilitates targeted technological adaptations in the direction of greater applicability and fidelity of simulation.

Modeling fibrous soft tissue dissection with elastic-plastic deformation for simulation of brain tumor removal

BACKGROUND AND OBJECTIVE

Realistic modeling the dissection of brain tissue is of key importance for simulation of brain tumor removal in virtual neurosurgery systems. However, existing methods are unable to characterize inelastic behaviors of brain tissue, such as plastic deformation and dissection evolution, making it ineffective in simulating brain tumor removal procedures.

METHODS

In this paper, a model of fibrous soft tissue dissection for the simulation of brain tumor removal is proposed. A dissection variable of representative volume element is used to characterize the dissection state of the fibrous soft tissue. The evolution of dissection with elastic-plastic deformation under the effects of external loads is presented.

RESULTS

Simulation results show that the proposed model provides realistic, stable and intuitive results in the simulation of fracture in fibrous soft tissues. As the external load increases, the fibrous soft tissue begins to crack, with the cracks growing and multiplying until they eventually merge to form a fracture. The proposed model is incorporated into the simulation of brain tumor removal.

CONCLUSIONS

The experimental results demonstrate the feasibility of modeling fibrous soft tissue dissection with elastic-plastic deformation. A relative high degree of realistic visual feedback is achieved.

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.

Virtual reality and surgical oncology

More than 80% of people diagnosed with cancer will require surgery. However, less than 5% have access to safe, affordable and timely surgery in low- and middle-income countries (LMICs) settings mostly due to the lack of trained workforce. Since its creation, virtual reality (VR) has been heralded as a viable adjunct to surgical training, but its adoption in surgical oncology to date is poorly understood. We undertook a systematic review to determine the application of VR across different surgical specialties, modalities and cancer pathway globally between January 2011 and 2021. We reviewed their characteristics and respective methods of validation of 24 articles. The results revealed gaps in application and accessibility of VR with a proclivity for high-income countries and high-risk, complex oncological surgeries. There is a lack of standardisation of clinical evaluation of VR, both in terms of clinical trials and implementation science. While all VR illustrated face and content validity, only around two-third exhibited construct validity and predictive validity was lacking overall. In conclusion, the asynchrony between VR development and actual global cancer surgery demand means the technology is not effectively, efficiently and equitably utilised to realise its surgical capacity-building potential. Future research should prioritise cost-effective VR technologies with predictive validity for high demand, open cancer surgeries required in LMICs.

Extended Reality in Neurosurgical Education: A Systematic Review

Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications.

Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation

BACKGROUND

The methodology of assessment and training of surgical skills is evolving to deal with the emergence of competency-based training. Artificial neural networks (ANNs), a branch of artificial intelligence, can use newly generated metrics not only for assessment performance but also to quantitate individual metric importance and provide new insights into surgical expertise.

OBJECTIVE

To outline the educational utility of using an ANN in the assessment and quantitation of surgical expertise. A virtual reality vertebral osteophyte removal during a simulated surgical spine procedure is used as a model to outline this methodology.

METHODS

Twenty-one participants performed a simulated anterior cervical diskectomy and fusion on the Sim-Ortho virtual reality simulator. Participants were divided into 3 groups, including 9 postresidents, 5 senior residents, and 7 junior residents. Data were retrieved from the osteophyte removal component of the scenario, which involved using a simulated burr. The data were manipulated to initially generate 83 performance metrics spanning 3 categories (safety, efficiency, and motion) of which only the most relevant metrics were used to train and test the ANN.

RESULTS

The ANN model was trained on 6 safety metrics to a testing accuracy of 83.3%. The contributions of these performance metrics to expertise were revealed through connection weight products and outlined 2 identifiable learning patterns of technical skills.

CONCLUSION

This study outlines the potential utility of ANNs which allows a deeper understanding of the composites of surgical expertise and may contribute to the paradigm shift toward competency-based surgical training.

Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study

BACKGROUND

Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training.

OBJECTIVE

To (1) provide validity for the simulated bimanual subpial tumor resection task and (2) to use this simulation in qualitative and quantitative evaluation of nondominant hand skills for bipolar forceps utilization.

METHODS

In this case series study, 45 right-handed participants performed a simulated subpial tumor resection using simulated bipolar forceps in the nondominant hand for assisting the surgery and hemostasis. A 10-item questionnaire was used to assess task validity. The nondominant hand skills across 4 expertise levels (neurosurgeons, senior trainees, junior trainees, and medical students) were analyzed by 2 visual models and performance metrics.

RESULTS

Neurosurgeon median (range) overall satisfaction with the simulated scenario was 4.0/5.0 (2.0-5.0). The visual models demonstrated a decrease in high force application areas on pial surface with increased expertise level. Bipolar-pia mater interactions were more focused around the tumoral region for neurosurgeons and senior trainees. These groups spent more time using the bipolar while interacting with pia. All groups spent significantly higher time in the left upper pial quadrant than other quadrants.

CONCLUSION

This work introduces new approaches for the evaluation of nondominant hand skills which may help surgical trainees by providing both qualitative and quantitative feedback.

Virtual and Augmented Reality in Neurosurgery: The Evolution of its Application and Study Designs

BACKGROUND

As the art of neurosurgery evolves in the 21st century, more emphasis is placed on minimally invasive techniques, which require technical precision. Simultaneously, the reduction on training hours continues, and teachers of neurosurgery faces "double jeopardy"-with harder skills to teach and less time to teach them. Mixed reality appears as the neurosurgical educators' natural ally: Virtual reality facilitates the learning of spatial relationships and permits rehearsal of skills, while augmented reality can make procedures safer and more efficient. Little wonder then, that the body of literature on mixed reality in neurosurgery has grown exponentially.

METHODS

Publications involving virtual and augmented reality in neurosurgery were examined. A total of 414 papers were included, and they were categorized according to study design and analyzed.

RESULTS

Half of the papers were published within the last 3 years alone. Whereas in the earlier half, most of the publications involved experiments in virtual reality simulation and the efficacy of skills acquisition, many of the more recent publication are proof-of-concept studies. This attests to the evolution of mixed reality in neurosurgery. As the technology advances, neurosurgeons are finding more applications, both in training and clinical practice.

CONCLUSIONS

With parallel advancement in Internet speed and artificial intelligence, the utilization of mixed reality will permeate neurosurgery. From solving staff problems in global neurosurgery, to mitigating the deleterious effect of duty-hour reductions, to improving individual operations, mixed reality will have a positive effect in many aspects of neurosurgery.

Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors.

Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure

BACKGROUND

Virtual reality surgical simulators are a safe and efficient technology for the assessment and training of surgical skills. Simulators allow trainees to improve specific surgical techniques in risk-free environments. Recently, machine learning has been coupled to simulators to classify performance. However, most studies fail to extract meaningful observations behind the classifications and the impact of specific surgical metrics on the performance. One benefit from integrating machine learning algorithms, such as Artificial Neural Networks, to simulators is the ability to extract novel insights into the composites of the surgical performance that differentiate levels of expertise.

OBJECTIVE

This study aims to demonstrate the benefits of artificial neural network algorithms in assessing and analyzing virtual surgical performances. This study applies the algorithm on a virtual reality simulated annulus incision task during an anterior cervical discectomy and fusion scenario.

DESIGN

An artificial neural network algorithm was developed and integrated. Participants performed the simulated surgical procedure on the Sim-Ortho simulator. Data extracted from the annulus incision task were extracted to generate 157 surgical performance metrics that spanned three categories (motion, safety, and efficiency).

SETTING

Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Center, McGill University, Montreal, Canada.

PARTICIPANTS

Twenty-three participants were recruited and divided into 3 groups: 11 post-residents, 5 senior and 7 junior residents.

RESULTS

An artificial neural network model was trained on nine selected surgical metrics, spanning all three categories and achieved 80% testing accuracy.

CONCLUSIONS

This study outlines the benefits of integrating artificial neural networks to virtual reality surgical simulators in understanding composites of expertise performance.

A systematic review of virtual reality for the assessment of technical skills in neurosurgery

OBJECTIVE

Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed.

METHODS

A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded.

RESULTS

Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems.

CONCLUSIONS

VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection.

Smart home R&D system based on virtual reality

Smart home products and equipment are relatively expensive while using specific physical objects to prove functional characteristics, the cost is high, and it is difficult to meet the personal needs of customers. Based on the above background, the purpose of this research is the application and design of a smart home R&D system based on virtual reality. This study proposes the concept of introducing virtual reality methods into the control scene given the shortcomings of the existing smart home control interface interaction methods. From the perspective of being more suitable for the user’s needs, the virtual reality method is used to optimize the smart home interaction methods. Through the analysis of the user’s lifestyle and needs, the functional module model of applying virtual reality to the smart home control scheme is established. Then, by collecting data, use Sketchup software to build and optimize the model of the simulation system to build a realistic family scene model. Finally, through the integrated use of the Unity 3D rendering engine and the virtual simulation system technology, the intelligent simulation of the interior functions of the house is realized. Experimental results show that using virtual reality to optimize the interaction of smart homes, the control method is relatively simple, and the cost can be reduced by about 20%.

Quantitation of Tissue Resection Using a Brain Tumor Model and 7-T Magnetic Resonance Imaging Technology

BACKGROUND

Animal brain tumor models can be useful educational tools for the training of neurosurgical residents in risk-free environments. Magnetic resonance imaging (MRI) technologies have not used these models to quantitate tumor, normal gray and white matter, and total tissue removal during complex neurosurgical procedures. This pilot study was carried out as a proof of concept to show the feasibility of using brain tumor models combined with 7-T MRI technology to quantitatively assess tissue removal during subpial tumor resection.

METHODS

Seven ex vivo calf brain hemispheres were used to develop the 7-T MRI segmentation methodology. Three brains were used to quantitate brain tissue removal using 7-T MRI segmentation methodology. Alginate artificial brain tumor was created in 4 calf brains to assess the ability of 7-T MRI segmentation methodology to quantitate tumor and gray and white matter along with total tissue volumes removal during a subpial tumor resection procedure.

RESULTS

Quantitative studies showed a correlation between removed brain tissue weights and volumes determined from segmented 7-T MRIs. Analysis of baseline and postresection alginate brain tumor segmented 7-T MRIs allowed quantification of tumor and gray and white matter along with total tissue volumes removed and detection of alterations in surrounding gray and white matter.

CONCLUSIONS

This pilot study showed that the use of animal tumor models in combination with 7-T MRI technology provides an opportunity to increase the granularity of data obtained from operative procedures and to improve the assessment and training of learners.

A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery

Background: Research proves that the apprenticeship model, which is the gold standard for training surgical residents, is obsolete. For that reason, there is a continuing effort toward the development of high-fidelity surgical simulators to replace the apprenticeship model. Applying Virtual Reality Augmented Reality (AR) and Mixed Reality (MR) in surgical simulators increases the fidelity, level of immersion and overall experience of these simulators.Areas covered: The objective of this review is to provide a comprehensive overview of the application of VR, AR and MR for distinct surgical disciplines, including maxillofacial surgery and neurosurgery. The current developments in these areas, as well as potential future directions, are discussed.Expert opinion: The key components for incorporating VR into surgical simulators are visual and haptic rendering. These components ensure that the user is completely immersed in the virtual environment and can interact in the same way as in the physical world. The key components for the application of AR and MR into surgical simulators include the tracking system as well as the visual rendering. The advantages of these surgical simulators are the ability to perform user evaluations and increase the training frequency of surgical residents.

Virtual Reality Anterior Cervical Discectomy and Fusion Simulation on the Novel Sim-Ortho Platform: Validation Studies

BACKGROUND

Virtual reality spine simulators are emerging as potential educational tools to assess and train surgical procedures in safe environments. Analysis of validity is important in determining the educational utility of these systems.

OBJECTIVE

To assess face, content, and construct validity of a C4-C5 anterior cervical discectomy and fusion simulation on the Sim-Ortho virtual reality platform, developed by OSSimTechTM (Montreal, Canada) and the AO Foundation (Davos, Switzerland).

METHODS

Spine surgeons, spine fellows, along with neurosurgical and orthopedic residents, performed a simulated C4-C5 anterior cervical discectomy and fusion on the Sim-Ortho system. Participants were separated into 3 categories: post-residents (spine surgeons and spine fellows), senior residents, and junior residents. A Likert scale was used to assess face and content validity. Construct validity was evaluated by investigating differences between the 3 groups on metrics derived from simulator data. The Kruskal-Wallis test was employed to compare groups and a post-hoc Dunn's test with a Bonferroni correction was utilized to investigate differences between groups on significant metrics.

RESULTS

A total of 21 individuals were included: 9 post-residents, 5 senior residents, and 7 junior residents. The post-resident group rated face and content validity, median ≥4, for the overall procedure and at least 1 tool in each of the 4 steps. Significant differences (P < .05) were found between the post-resident group and senior and/or junior residents on at least 1 metric for each component of the simulation.

CONCLUSION

The C4-C5 anterior cervical discectomy and fusion simulation on the Sim-Ortho platform demonstrated face, content, and construct validity suggesting its utility as a formative educational tool.

Three-Dimensional Printed Ergonomically Improved Microforceps for Microneurosurgery

OBJECTIVE

The aim of this study was to develop and validate an ergonomically improved microforceps, which is a neurosurgical instrument used in microscopic procedures. The distance between tips of microforceps becomes large at high magnification of the operating microscope. This results in tips moving out of view and causes ergonomic discomfort.

METHODS

The design criteria for ergonomic microforceps were defined, which primarily involved a reduction in the distance between tips and applied force. Computer models of the existing and modified microforceps were created and fabricated using direct metal laser sintering. Ten neurosurgeons validated the developed instrument and provided feedback. In objective validation, video feed of the operating microscope was marked and analyzed by an expert neurosurgeon.

RESULTS

In subjective validation, most of the neurosurgeons endorsed the ergonomic improvements. The parameters, including microforceps tips moving out of view (P = 0.0005), suture holding attempts (P = 0.001), and needle holding attempts (P = 0.03), were found to be statistically improved (Mann-Whitney U test), whereas the average time taken to tie 1 knot was not statistically improved (P = 0.06). The ergonomic modification also resulted in a reduction of applied force by 47.5%.

CONCLUSIONS

Validation results show that the developed instrument provides several ergonomic benefits for the microsuturing task under high magnification of the operating microscope.

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.

Roadmap for Developing Complex Virtual Reality Simulation Scenarios: Subpial Neurosurgical Tumor Resection Model

BACKGROUND

Advancement and evolution of current virtual reality (VR) surgical simulation technologies are integral to improve the available armamentarium of surgical skill education. This is especially important in high-risk surgical specialties. Such fields including neurosurgery are beginning to explore the utilization of virtual reality simulation in the assessment and training of psychomotor skills. An important issue facing the available VR simulation technologies is the lack of complexity of scenarios that fail to replicate the visual and haptic realities of complex neurosurgical procedures. Therefore there is a need to create more realistic and complex scenarios with the appropriate visual and haptic realities to maximize the potential of virtual reality technology.

METHODS

We outline a roadmap for creating complex virtual reality neurosurgical simulation scenarios using a step-wise description of our team's subpial tumor resection project as a model.

RESULTS

The creation of complex neurosurgical simulations involves integrating multiple modules into a scenario-building roadmap. The components of each module are described outlining the important stages in the process of complex VR simulation creation.

CONCLUSIONS

Our roadmap of a stepwise approach for the creation of complex VR-simulated neurosurgical procedures may also serve as a guide to aid the development of other VR scenarios in a variety of surgical fields. The generation of new VR complex simulated neurosurgical procedures, by surgeons for surgeons, with the help of computer scientists and engineers may improve the assessment and training of residents and ultimately improve patient care.

Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance

BACKGROUND

Virtual reality surgical simulators provide a safe environment for trainees to practice specific surgical scenarios and allow for self-guided learning. Artificial intelligence technology, including artificial neural networks, offers the potential to manipulate large datasets from simulators to gain insight into the importance of specific performance metrics during simulated operative tasks.

OBJECTIVE

To distinguish performance in a virtual reality-simulated anterior cervical discectomy scenario, uncover novel performance metrics, and gain insight into the relative importance of each metric using artificial neural networks.

METHODS

Twenty-one participants performed a simulated anterior cervical discectomy on the novel virtual reality Sim-Ortho simulator. Participants were divided into 3 groups, including 9 post-resident, 5 senior, and 7 junior participants. This study focused on the discectomy portion of the task. Data were recorded and manipulated to calculate metrics of performance for each participant. Neural networks were trained and tested and the relative importance of each metric was calculated.

RESULTS

A total of 369 metrics spanning 4 categories (safety, efficiency, motion, and cognition) were generated. An artificial neural network was trained on 16 selected metrics and tested, achieving a training accuracy of 100% and a testing accuracy of 83.3%. Network analysis identified safety metrics, including the number of contacts on spinal dura, as highly important.

CONCLUSION

Artificial neural networks classified 3 groups of participants based on expertise allowing insight into the relative importance of specific metrics of performance. This novel methodology aids in the understanding of which components of surgical performance predominantly contribute to expertise.

Artificial Intelligence Distinguishes Surgical Training Levels in a Virtual Reality Spinal Task

BACKGROUND

With the emergence of competency-based training, the current evaluation scheme of surgical skills is evolving to include newer methods of assessment and training. Artificial intelligence through machine learning algorithms can utilize extensive data sets to analyze operator performance. This study aimed to address 3 questions: (1) Can artificial intelligence uncover novel metrics of surgical performance? (2) Can support vector machine algorithms be trained to differentiate "senior" and "junior" participants who are executing a virtual reality hemilaminectomy? (3) Can other algorithms achieve a good classification performance?

METHODS

Participants from 4 Canadian universities were divided into 2 groups according to their training level (senior and junior) and were asked to perform a virtual reality hemilaminectomy. The position, angle, and force application of the simulated burr and suction instruments, along with tissue volumes that were removed, were recorded at 20-ms intervals. Raw data were manipulated to create metrics to train machine learning algorithms. Five algorithms, including a support vector machine, were trained to predict whether the task was performed by a senior or junior participant. The accuracy of each algorithm was assessed through leave-one-out cross-validation.

RESULTS

Forty-one individuals were enrolled (22 senior and 19 junior participants). Twelve metrics related to safety of the procedure, efficiency, motion of the tools, and coordination were selected. Following cross-validation, the support vector machine achieved a 97.6% accuracy. The other algorithms achieved accuracy of 92.7%, 87.8%, 70.7%, and 65.9%, respectively.

CONCLUSIONS

Artificial intelligence defined novel metrics of surgical performance and outlined training levels in a virtual reality spinal simulation procedure.

CLINICAL RELEVANCE

The significance of these results lies in the potential of artificial intelligence to complement current educational paradigms and better prepare residents for surgical 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.

A Comparison of Visual Rating Scales and Simulated Virtual Reality Metrics in Neurosurgical Training: A Generalizability Theory Study

BACKGROUND

Adequate assessment and feedback remains a cornerstone of psychomotor skills acquisition, particularly within neurosurgery where the consequence of adverse operative events is significant. However, a critical appraisal of the reliability of visual rating scales in neurosurgery is lacking. Therefore, we sought to design a study to compare visual rating scales with simulated metrics in a neurosurgical virtual reality task.

METHODS

Neurosurgical faculty rated anonymized participant video recordings of the removal of simulated brain tumors using a visual rating scale made up of seven composite elements. Scale reliability was evaluated using generalizability theory, and scale subcomponents were compared with simulated metrics using Pearson correlation analysis.

RESULTS

Four staff neurosurgeons evaluated 16 medical student neurosurgery applicants. Overall scale reliability and internal consistency were 0.73 and 0.90, respectively. Reliability of 0.71 was achieved with two raters. Individual participants, raters, and scale items accounted for 27%, 11%, and 0.6% of the data variability. The hemostasis scale component related to the greatest number of simulated metrics, whereas respect for no-go zones and tissue was correlated with none. Metrics relating to instrument force and patient safety (brain volume removed and blood loss) were captured by the fewest number of rating scale components.

CONCLUSIONS

To our knowledge, this is the first study comparing participant's ratings with simulated performance. Given rating scales capture less well instrument force, quantity of brain volume removed, and blood loss, we suggest adopting a hybrid educational approach using visual rating scales in an operative environment, supplemented by simulated sessions to uncover potentially problematic surgical technique.

Virtual reality and augmented reality in the management of intracranial tumors: A review

Neurosurgeons are faced with the challenge of planning, performing, and learning complex surgical procedures. With improvements in computational power and advances in visual and haptic display technologies, augmented and virtual surgical environments can offer potential benefits for tests in a safe and simulated setting, as well as improve management of real-life procedures. This systematic literature review is conducted in order to investigate the roles of such advanced computing technology in neurosurgery subspecialization of intracranial tumor removal. The study would focus on an in-depth discussion on the role of virtual reality and augmented reality in the management of intracranial tumors: the current status, foreseeable challenges, and future developments.

Is Virtual Reality Surgical Performance Influenced by Force Feedback Device Utilized?

OBJECTIVE

The study objectives were to assess if surgical performance and subjective assessment of a virtual reality simulator platform was influenced by changing force feedback devices.

DESIGN

Participants used the NeuroVR (formerly NeuroTouch) simulator to perform 5 practice scenarios and a realistic scenario involving subpial resection of a virtual reality brain tumor with simulated bleeding. The influence of force feedback was assessed by utilizing the Omni and Entact haptic systems. Tier 1, tier 2, and tier 2 advanced metrics were used to compare results. Operator subjective assessment of the haptic systems tested utilized seven Likert criteria (score 1 to 5).

SETTING

The study is carried out at the McGill Neurosurgical Simulation Research and Training Centre, Montreal Neurological Institute and Hospital, Montreal, Canada.

PARTICIPANTS

Six expert operators in the utilization of the NeuroVR simulator platform.

RESULTS

No significant differences in surgical performance were found between the two haptic devices. Participants significantly preferred the Entact system on all 7 Likert criteria of subjective assessment.

CONCLUSIONS

Our results show no statistical differences in virtual reality surgical performance utilizing the two bimanual haptic devices tested. Subjective assessments demonstrated that participants preferred the Entact system. Our results suggest that to maximize realism of the training experience educators employing virtual reality simulators may find it useful to assess expert opinion before choosing a force feedback device.

Virtual reality simulation of robotic transsphenoidal brain tumor resection: Evaluating dynamic motion scaling in a master-slave system

Background

Integrating simulators with robotic surgical procedures could assist in designing and testing of novel robotic control algorithms and further enhance patient‐specific pre‐operative planning and training for robotic surgeries.

Methods

A virtual reality simulator, developed to perform the transsphenoidal resection of pituitary gland tumours, tested the usability of robotic interfaces and control algorithms. It used position‐based dynamics to allow soft‐tissue deformation and resection with haptic feedback; dynamic motion scaling control was also incorporated into the simulator.

Results

Neurosurgeons and residents performed the surgery under constant and dynamic motion scaling conditions (CMS vs DMS). DMS increased dexterity and reduced the risk of damage to healthy brain tissue. Post‐experimental questionnaires indicated that the system was well‐evaluated by experts.

Conclusion

The simulator was intuitively and realistically operated. It increased the safety and accuracy of the procedure without affecting intervention time. Future research can investigate incorporating this simulation into a real micro‐surgical robotic system.

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.

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.

The Effect of 3-Dimensional Simulation on Neurosurgical Skill Acquisition and Surgical Performance: A Review of the Literature

OBJECTIVE

In recent years, 3-dimensional (3D) simulation of neurosurgical procedures has become increasingly popular as an addition to training programmes. However, there remains little objective evidence of its effectiveness in improving live surgical skill. This review analysed the current literature in 3D neurosurgical simulation, highlighting remaining gaps in the evidence base for improvement in surgical performance and suggests useful future research directions.

DESIGN

An electronic search of the databases was conducted to identify studies investigating 3D virtual reality (VR) simulation for various types of neurosurgery. Eligible studies were those that used a combination of metrics to measure neurosurgical skill acquisition on a simulation trainer. Studies were excluded if they did not measure skill acquisition against a set of metrics or if they assessed skills that were not used in neurosurgical practice. This was not a systematic review however, the data extracted was tabulated to allow comparison between studies RESULTS: This study revealed that the average overall quality of the included studies was moderate. Only one study assessed outcomes in live surgery, while most other studies assessed outcomes on a simulator using a variety of metrics.

CONCLUSIONS

It is concluded that in its current state, the evidence for 3D simulation suggests it as a useful supplement to training programmes but more evidence is needed of improvement in surgical performance to warrant large-scale investment in this technology.

Training in Cerebral Aneurysm Clipping Using Self-Made 3-Dimensional Models

INTRODUCTION

Recently, there have been increasingly fewer opportunities for junior surgeons to receive on-the-job training. Therefore, we created custom-built three-dimensional (3D) surgical simulators for training in connection with cerebral aneurysm clipping.

METHODS

Three patient-specific models were composed of a trimmed skull, retractable brain, and a hollow elastic aneurysm with its parent artery. The brain models were created using 3D printers via a casting technique. The artery models were made by 3D printing and a lost-wax technique. Four residents and 2 junior neurosurgeons attended the training courses. The trainees retracted the brain, observed the parent arteries and aneurysmal neck, selected the clip(s), and clipped the neck of an aneurysm. The duration of simulation was recorded. A senior neurosurgeon then assessed the trainee's technical skill and explained how to improve his/her performance for the procedure using a video of the actual surgery. Subsequently, the trainee attempted the clipping simulation again, using the same model. After the course, the senior neurosurgeon assessed each trainee's technical skill. The trainee critiqued the usefulness of the model and the effectiveness of the training course.

RESULTS

Trainees succeeded in performing the simulation in line with an actual surgery. Their skills tended to improve upon completion of the training.

CONCLUSION

These simulation models are easy to create, and we believe that they are very useful for training junior neurosurgeons in the surgical techniques needed for cerebral aneurysm clipping.

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.

An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education

The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee’s performance. For this reason, it is necessary to develop automatic mechanisms to estimate metrics for assessing manual dexterity in a quantitative way. Authors have proposed some hardware-software approaches to evaluate manual dexterity on surgical simulators. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. It has been designed to be used with a neurosurgical simulator called Daubara NS Trainer, but can be easily adapted to another benchtop- and manikin-based medical simulators. The system was tested with a sample of 14 volunteers who performed a test that was designed to simultaneously evaluate their fine motor skills and the IGlove’s functionalities. Metrics obtained by each of the participants are presented as results in this work; it is also shown how these metrics are used to automatically evaluate the level of manual dexterity of each volunteer.

Texture analysis of the developing human brain using customization of a knowledge-based system

Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy.Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs.Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.

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.

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.

Utilizing virtual and augmented reality for educational and clinical enhancements in neurosurgery

Neurosurgery has undergone a technological revolution over the past several decades, from trephination to image-guided navigation. Advancements in virtual reality (VR) and augmented reality (AR) represent some of the newest modalities being integrated into neurosurgical practice and resident education. In this review, we present a historical perspective of the development of VR and AR technologies, analyze its current uses, and discuss its emerging applications in the field of neurosurgery.

A Low-Cost, Passive Navigation Training System for Image-Guided Spinal Intervention

BACKGROUND

Navigation technology is used for training in various medical specialties, not least image-guided spinal interventions. Navigation practice is an important educational component that allows residents to understand how surgical instruments interact with complex anatomy and to learn basic surgical skills such as the tridimensional mental interpretation of bidimensional data. Inexpensive surgical simulators for spinal surgery, however, are lacking. We therefore designed a low-cost spinal surgery simulator (Spine MovDigSys 01) to allow 3-dimensional navigation via 2-dimensional images without altering or limiting the surgeon's natural movement.

METHODS

A training system was developed with an anatomical lumbar model and 2 webcams to passively digitize surgical instruments under MATLAB software control. A proof-of-concept recognition task (vertebral body cannulation) and a pilot test of the system with 12 neuro- and orthopedic surgeons were performed to obtain feedback on the system. Position, orientation, and kinematic variables were determined and the lateral, posteroanterior, and anteroposterior views obtained.

RESULTS

The system was tested with a proof-of-concept experimental task. Operator metrics including time of execution (t), intracorporeal length (d), insertion angle (α), average speed (v¯), and acceleration (a) were obtained accurately. These metrics were converted into assessment metrics such as smoothness of operation and linearity of insertion. Results from initial testing are shown and the system advantages and disadvantages described.

CONCLUSIONS

This low-cost spinal surgery training system digitized the position and orientation of the instruments and allowed image-guided navigation, the generation of metrics, and graphic recording of the instrumental route. Spine MovDigSys 01 is useful for development of basic, noninnate skills and allows the novice apprentice to quickly and economically move beyond the basics.

Face, content, and construct validity of human placenta as a haptic training tool in neurointerventional surgery

OBJECT

The development of neurointerventional treatments of central nervous system disorders has resulted in the need for adequate training environments for novice interventionalists. Virtual simulators offer anatomical definition but lack adequate tactile feedback. Animal models, which provide more lifelike training, require an appropriate infrastructure base. The authors describe a training model for neurointerventional procedures using the human placenta (HP), which affords haptic training with significantly fewer resource requirements, and discuss its validation.

METHODS

Twelve HPs were prepared for simulated endovascular procedures. Training exercises performed by interventional neuroradiologists and novice fellows were placental angiography, stent placement, aneurysm coiling, and intravascular liquid embolic agent injection.

RESULTS

The endovascular training exercises proposed can be easily reproduced in the HP. Face, content, and construct validity were assessed by 6 neurointerventional radiologists and 6 novice fellows in interventional radiology.

CONCLUSIONS

The use of HP provides an inexpensive training model for the training of neurointerventionalists. Preliminary validation results show that this simulation model has face and content validity and has demonstrated construct validity for the interventions assessed in this study.

Training for brain tumour resection: a realistic model with easy accessibility

BACKGROUND

Resection of intrinsic and extrinsic brain tumours requires an understanding of sulcal and gyral anatomy, familiarity with tissue consistency and tissue manipulation. As yet, these skills are acquired by observation and supervised manipulation during surgery, thus accepting a potential learning curve at the expense of the patient in a live surgical situation. A brain tumour model could ensure optimised manual skills and understanding of surgical anatomy acquired in an elective and relaxed teaching situation. We report and evaluate a brain tumour model, regarding availability, realistic representation of sulcal and gyral anatomy and tissue consistency.

METHOD

Freshly prepared agar-agar solution with different concentrations was added with highlighter ink and injected into fresh sheep brains.

RESULTS

Hardened agar-agar solution formed masses comparable to malignant brain tumours. Variation of the agar-agar concentration influenced diffusion of agar-agar solution in the adjacent brain tissue. Higher concentrated agar-agar solutions formed sharply delimitated masses mimicking cerebral metastases and lower concentrated agar-agar solutions tended to diffuse into the adjacent cerebral tissue. Adding highlighter ink to the agar-agar solution produced fluorescence after blue light excitation comparable to the 5-ALA induced fluorescence of malignant glioma.

CONCLUSIONS

The described in vitro sheep brain tumour model is simple and realistic, available practically everywhere and cheap. Therefore, it could be useful for young neurosurgical residents to acquire basic neuro-oncological skills, experiencing properties of the cerebral brain texture and its haptic perception and to learn handling of neurosurgical equipment.

Face, Content, and Construct Validity of Brain Tumor Microsurgery Simulation Using a Human Placenta Model

BACKGROUND

Brain tumors are complex 3-dimensional lesions. Their resection involves training and the use of the multiple microsurgical techniques available for removal. Simulation models, with haptic and visual realism, may be useful for improving the bimanual technical skills of neurosurgical residents and neurosurgeons, potentially decreasing surgical errors and thus improving patient outcomes.

OBJECTIVE

To describe and assess an ex vivo placental model for brain tumor microsurgery using a simulation tool in neurosurgical psychomotor teaching and assessment.

METHODS

Sixteen human placentas were used in this research project. Intravascular blood remnants were removed by continuous saline solution irrigation of the 2 placental arteries and placental vein. Brain tumors were simulated using silicone injections in the placental stroma. Eight neurosurgeons and 8 neurosurgical residents carried out the resection of simulated tumors using the same surgical instruments and bimanual microsurgical techniques used to perform human brain tumor operations. Face and content validity was assessed using a subjective evaluation based on a 5-point Likert scale. Construct validity was assessed by analyzing the surgical performance of the neurosurgeon and resident groups.

RESULTS

The placenta model simulated brain tumor surgical procedures with high fidelity. Results showed face and content validity. Construct validity was demonstrated by statistically different surgical performances among the evaluated groups.

CONCLUSION

Human placentas are useful haptic models to simulate brain tumor microsurgical removal. Results using this model demonstrate face, content, and construct validity.