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

Artificial intelligence innovations in neurosurgical oncology: a narrative review

PURPOSE

Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.

METHODS

A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors.

RESULTS

Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens.

CONCLUSION

While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.

The Integration of 3D Virtual Reality and 3D Printing Technology as Innovative Approaches to Preoperative Planning in Neuro-Oncology

Our study explores the integration of three-dimensional (3D) virtual reality (VR) and 3D printing in neurosurgical preoperative planning. Traditionally, surgeons relied on two-dimensional (2D) imaging for complex neuroanatomy analyses, requiring significant mental visualization. Fortunately, nowadays advanced technology enables the creation of detailed 3D models from patient scans, utilizing different software. Afterwards, these models can be experienced through VR systems, offering comprehensive preoperative rehearsal opportunities. Additionally, 3D models can be 3D printed for hands-on training, therefore enhancing surgical preparedness. This technological integration transforms the paradigm of neurosurgical planning, ensuring safer procedures.

Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation

BACKGROUND AND OBJECTIVES

Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment.

METHODS

An ex vivo calf brain simulation model was developed in which trainees performed a subpial corticectomy of three defined areas. A case series study assessed face and content validity of the model using 7-point Likert scale questionnaires.

RESULTS

Twelve skilled and 11 less skilled participants were included in this investigation. Overall median scores of 6.0 (range 4.0-6.0) for face validity and 6.0 (range 3.5-7.0) for content validity were determined on the 7-point Likert scale, with no statistical differences between skilled and less skilled groups identified.

CONCLUSION

A novel ex vivo calf brain simulator was developed to replicate the subpial resection procedure and demonstrated face and content validity.

Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation

BACKGROUND AND OBJECTIVES

Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning.

METHODS

Annotated images from a publicly available video data set of surgeons managing endoscopic endonasal carotid artery lacerations in a perfused cadaveric simulator were collected. A deep learning model was implemented to detect surgical instruments across video frames. ShEn score for the instrument sequence was calculated from each surgical trial. Logistic regression using ShEn was used to predict hemorrhage control success.

RESULTS

ShEn scores and instrument usage patterns differed between successful and unsuccessful trials (ShEn: 0.452 vs 0.370, P < .001). Unsuccessful hemorrhage control trials displayed lower entropy and less varied instrument use patterns. By contrast, successful trials demonstrated higher entropy with more diverse instrument usage and consistent progression in instrument utilization. A logistic regression model using ShEn scores (78% accuracy and 97% average precision) was at least as accurate as surgeons' attending/resident status and years of experience for predicting trial success and had similar accuracy as expert human observers.

CONCLUSION

ShEn score offers a summative signal about surgeon performance and predicted success at controlling carotid hemorrhage in a simulated cadaveric setting. Future efforts to generalize ShEn to additional surgical scenarios can further validate this metric.

Becoming a neurosurgeon in France: A qualitative study from the trainees' perspective

Introduction

The training of neurosurgeons is evolving in a world of socio-professional changes, including the technological revolution, administrative pressure on stakeholders, reduced working hours, geographical heterogeneity, generational changes, to name but a few.

Research question

This qualitative study aimed to explore experiences and feedback of French neurosurgical trainees concerning their training.

Material and methods

The grounded theory approach was used with 23 neurosurgical trainees' interviews. Inclusion was continued until data saturation. Six researchers (an anthropologist, a psychiatrist, and four neurosurgeons) thematically and independently analyzed data collected through anonymized interviews.

Results

Data analysis identified three superordinate themes: (1) The Trainee-Senior Dyad, where the respondents describe a similar bipolarity between trainees and faculty (trainees oscillating between those who fit into the system and those who are more reluctant to accept hierarchy, faculty using an ideal pedagogy while others refuse to help or invest in training); (2) The difficulty to learn (describing pressure exercised on trainees that can alter their motivation and degrade their training, including the impact of administrative tasks); (3) A pedagogy of empowerment (trainee' feelings about the pertinent pedagogy in the OR, ideal sequence to progress, progressive empowerment especially during the shifts, and stress of envisioning themselves as a senior neurosurgeon).

Discussion and conclusion

Respondents emphasize the heterogeneity of their training both intra- and inter-university-hospital. Their critical analysis, as well as the formalization of their stress to become autonomous seniors, can be an important link with the reforms and optimizations currently being carried out to improve and standardize the training of young French neurosurgeons.

Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery

Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.

Investigating and acquiring motor expertise using virtual reality

After just months of simulated training, on January 19, 2019 a 23-year-old E-sports pro-gamer, Enzo Bonito, took to the racetrack and beat Lucas di Grassi, a Formula E and ex-Formula 1 driver with decades of real-world racing experience. This event raised the possibility that practicing in virtual reality can be surprisingly effective for acquiring motor expertise in real-world tasks. Here, we evaluate the potential of virtual reality to serve as a space for training to expert levels in highly complex real-world tasks in time windows much shorter than those required in the real world and at much lower financial cost without the hazards of the real world. We also discuss how VR can also serve as an experimental platform for exploring the science of expertise more generally.

Expanding Educational Frontiers in Neurosurgery: Current and Future Uses of Augmented Reality

BACKGROUND

Augmented reality (AR) technology is a new and promising option to advance and expand neurosurgical training because of recent advances in computer vision technology, improved AR software and hardware, and growing acceptance of this technology in clinical practice.

OBJECTIVE

To analyze the current status of AR use cases with the goal of envisioning future uses of AR in neurosurgical education.

METHODS

Articles applying to AR technology use in neurosurgical education were identified using PubMed, Google Scholar, and Web of Science databases following the Preferred Reporting Items of Systematic Reviews and Meta-Analyses guidelines. Articles were included for review based on applicable content related to neurosurgical or neuroanatomy training. Assessment of literature quality was completed using standardized MERSQI scoring.

RESULTS

The systematic search identified 2648 unique articles. Of these, 12 studies met inclusion criteria after extensive review. The average MERSQI score was 10.2 (SD: 1.7). The most common AR platform identified in this study was the Microsoft Hololens. The primary goals of the studies were to improve technical skills and approaches to surgical planning or improve understanding of neuroanatomy.

CONCLUSION

Augmented reality has emerged as a promising training tool in neurosurgery. This is demonstrated in the wide range of cases in technical training and anatomic education. It remains unclear how AR-based training compares directly with traditional training methods; however, AR shows great promise in the ability to further enhance and innovate neurosurgical education and training.

Quantifying the Impact of Signal-to-background Ratios on Surgical Discrimination of Fluorescent Lesions

PURPOSE

Surgical fluorescence guidance has gained popularity in various settings, e.g., minimally invasive robot-assisted laparoscopic surgery. In pursuit of novel receptor-targeted tracers, the field of fluorescence-guided surgery is currently moving toward increasingly lower signal intensities. This highlights the importance of understanding the impact of low fluorescence intensities on clinical decision making. This study uses kinematics to investigate the impact of signal-to-background ratios (SBR) on surgical performance.

METHODS

Using a custom grid exercise containing hidden fluorescent targets, a da Vinci Xi robot with Firefly fluorescence endoscope and ProGrasp and Maryland forceps instruments, we studied how the participants' (N = 16) actions were influenced by the fluorescent SBR. To monitor the surgeon's actions, the surgical instrument tip was tracked using a custom video-based tracking framework. The digitized instrument tracks were then subjected to multi-parametric kinematic analysis, allowing for the isolation of various metrics (e.g., velocity, jerkiness, tortuosity). These were incorporated in scores for dexterity (Dx), decision making (DM), overall performance (PS) and proficiency. All were related to the SBR values.

RESULTS

Multi-parametric analysis showed that task completion time, time spent in fluorescence-imaging mode and total pathlength are metrics that are directly related to the SBR. Below SBR 1.5, these values substantially increased, and handling errors became more frequent. The difference in Dx and DM between the targets that gave SBR < 1.50 and SBR > 1.50, indicates that the latter group generally yields a 2.5-fold higher Dx value and a threefold higher DM value. As these values provide the basis for the PS score, proficiency could only be achieved at SBR > 1.55.

CONCLUSION

By tracking the surgical instruments we were able to, for the first time, quantitatively and objectively assess how the instrument positioning is impacted by fluorescent SBR. Our findings suggest that in ideal situations a minimum SBR of 1.5 is required to discriminate fluorescent lesions, a substantially lower value than the SBR 2 often reported in literature.

Usability of mixed reality in awake craniotomy planning

PURPOSE

This study aimed to describe our institutional use of a commercially available mixed reality viewer within a multi-disciplinary planning workflow for awake craniotomy surgery and to report an assessment of its usability.

MATERIALS AND METHODS

Three Tesla MRI scans, including 32-direction diffusion tensor sequences, were reconstructed with BrainLab Elements auto-segmentation software. Magic Leap mixed reality viewer headsets were registered to a shared virtual viewing space to display image reconstructions. System Usability Scale was used to assess the usability of the mixed reality system.

RESULTS

The awake craniotomy planning workflow utilises the mixed reality viewer to facilitate a stepwise discussion through four progressive anatomical layers; the skin, cerebral cortex, subcortical white matter tracts and tumour with surrounding vasculature. At each stage relevant members of the multi-disciplinary team review key operative considerations, including patient positioning, cortical and subcortical speech mapping protocols and surgical approaches to the tumour.The mixed reality system was used for multi-disciplinary awake craniotomy planning in 10 consecutive procedures over a 5-month period. Ten participants (2 Anaesthetists, 5 Neurosurgical trainees, 2 Speech therapists, 1 Neuropsychologist) completed System Usability Scale assessments, reporting a mean score of 71.5. Feedback highlighted the benefit of being able to rehearse important steps in the procedure, including patient positioning and anaesthetic access and visualising the testing protocol for cortical and subcortical speech mapping.

CONCLUSIONS

This study supports the use of mixed reality for multidisciplinary planning for awake craniotomy surgery, with an acceptable degree of usability of the interface. We highlight the need to consider the requirements of non-technical, non-neurosurgical team members when involving mixed reality activities.

History of Virtual Reality and Augmented Reality in Neurosurgical Training

Virtual reality (VR) and augmented reality (AR) are rapidly growing technologies. Both have been applied within neurosurgery for presurgical planning and intraoperative navigation, but VR and AR technology is particularly promising for the education of neurosurgical trainees. With the increasing demand for high impact yet efficient educational strategies, VR- and AR-based simulators allow neurosurgical residents to practice technical skills in a low-risk setting. Initial studies have confirmed that such simulators increase trainees' confidence, improve their understanding of operative anatomy, and enhance surgical techniques. Knowledge of the history and conceptual underpinnings of these technologies is useful to understand their current and future applications towards neurosurgical training. The technological precursors for VR and AR were introduced as early as the 1800s, and draw from the fields of entertainment, flight simulation, and education. However, computer software and processing speeds are needed to develop widespread VR- and AR-based surgical simulators, which have only been developed within the last 15 years. During that time, several devices had become rapidly adopted by neurosurgeons, and some programs had begun to incorporate them into the residency curriculum. With ever-improving technology, VR and AR are promising additions to a multi-modal training program, enabling neurosurgical residents to maximize their efforts in preparation for the operating room. In this review, we outline the historical development of the VR and AR systems that are used in neurosurgical training and discuss representative examples of the current technology.

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence

OBJECTIVE

Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups.

METHODS

A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students. All participants performed 5 repetitions of a subpial tumor resection on the NeuroVR (CAE Healthcare) platform, and 6 a priori-derived metrics selected using the K-nearest neighbors machine learning algorithm were used to assess participant learning curves. Group learning curves were plotted over the 5 trials for each metric. A mixed, repeated-measures ANOVA was performed between the first and fifth trial. For significant interactions (p < 0.05), post hoc Tukey's HSD analysis was conducted to determine the location of the significance.

RESULTS

Overall, 5 of the 6 metrics assessed had a significant interaction (p < 0.05). The 4 groups, neurosurgeons, seniors, juniors, and medical students, showed an improvement between the first and fifth trial on at least one of the 6 metrics evaluated.

CONCLUSIONS

Learning curves generated using AI-derived metrics provided novel insights into technical skill acquisition, based on expertise level, during repeated VR-simulated subpial tumor resections, which will allow educators to develop more focused formative educational paradigms for neurosurgical trainees.

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.

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.

Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study

OBJECTIVE

While the utilization of machine learning (ML) for data analysis typically requires significant technical expertise, novel platforms can deploy ML methods without requiring the user to have any coding experience (termed AutoML). The potential for these methods to be applied to neurosurgical video and surgical data science is unknown.

METHODS

AutoML, a code-free ML (CFML) system, was used to identify surgical instruments contained within each frame of endoscopic, endonasal intraoperative video obtained from a previously validated internal carotid injury training exercise performed on a high-fidelity cadaver model. Instrument-detection performances using CFML were compared with two state-of-the-art ML models built using the Python coding language on the same intraoperative video data set.

RESULTS

The CFML system successfully ingested surgical video without the use of any code. A total of 31,443 images were used to develop this model; 27,223 images were uploaded for training, 2292 images for validation, and 1928 images for testing. The mean average precision on the test set across all instruments was 0.708. The CFML model outperformed two standard object detection networks, RetinaNet and YOLOv3, which had mean average precisions of 0.669 and 0.527, respectively, in analyzing the same data set. Significant advantages to the CFML system included ease of use, relatively low cost, displays of true/false positives and negatives in a user-friendly interface, and the ability to deploy models for further analysis with ease. Significant drawbacks of the CFML model included an inability to view the structure of the trained model, an inability to update the ML model once trained with new examples, and the inability for robust downstream analysis of model performance and error modes.

CONCLUSIONS

This first report describes the baseline performance of CFML in an object detection task using a publicly available surgical video data set as a test bed. Compared with standard, code-based object detection networks, CFML exceeded performance standards. This finding is encouraging for surgeon-scientists seeking to perform object detection tasks to answer clinical questions, perform quality improvement, and develop novel research ideas. The limited interpretability and customization of CFML models remain ongoing challenges. With the further development of code-free platforms, CFML will become increasingly important across biomedical research. Using CFML, surgeons without significant coding experience can perform exploratory ML analyses rapidly and efficiently.

Tool-Tissue Forces in Hemangioblastoma Surgery

OBJECTIVE

Surgical resection of intracranial hemangioblastoma poses technical challenges that may be difficult to impart to trainees. Here, we introduce knowledge of tool-tissue forces in Newton (N), observed during hemangioblastoma surgery.

METHODS

Seven surgeons (2 groups: trainees and mentor), with mentor (n = 1) and trainees (n = 6, PGY 1-6 including clinical fellowship), participated in 6 intracranial hemangioblastoma surgeries. Using sensorized bipolar forceps, we evaluated tool-tissue force profiles of 5 predetermined surgical tasks: 1) dissection, 2) coagulation, 3) retracting, 4) pulling, and 5) manipulating. Force profile for each trial included force duration, average, maximum, minimum, range, standard deviation (SD), and correlation coefficient. Force errors including unsuccessful trial bleeding or incomplete were compared between surgeons and with successful trials.

RESULTS

Force data from 718 trials were collected. The mean (standard deviation) of force used in all surgical tasks and across all surgical levels was 0.20 ± 0.17 N. The forces exerted by trainee surgeons were significantly lower than those of the mentor (0.15 vs. 0.24; P < 0.0001). A total of 18 (4.5%) trials were unsuccessful, 4 of them being unsuccessful trial-bleeding and the rest, unsuccessful trial-incomplete. The force in unsuccessful trial-bleeding was higher than successful trials (0.3 [0.09] vs. 0.17 [0.11]; P = 0.0401). Toward the end of surgery, higher force was observed (0.17 vs. 0.20; P < 0.0001).

CONCLUSIONS

The quantification of tool-tissue forces during hemangioblastoma surgery with feedback to the surgeon, could well enhance surgical training and allow avoidance of bleeding associated with high force error.

Virtual Reality in Neurosurgery: Beyond Neurosurgical Planning

Background: While several publications have focused on the intuitive role of augmented reality (AR) and virtual reality (VR) in neurosurgical planning, the aim of this review was to explore other avenues, where these technologies have significant utility and applicability. Methods: This review was conducted by searching PubMed, PubMed Central, Google Scholar, the Scopus database, the Web of Science Core Collection database, and the SciELO citation index, from 1989–2021. An example of a search strategy used in PubMed Central is: “Virtual reality” [All Fields] AND (“neurosurgical procedures” [MeSH Terms] OR (“neurosurgical” [All Fields] AND “procedures” [All Fields]) OR “neurosurgical procedures” [All Fields] OR “neurosurgery” [All Fields] OR “neurosurgery” [MeSH Terms]). Using this search strategy, we identified 487 (PubMed), 1097 (PubMed Central), and 275 citations (Web of Science Core Collection database). Results: Articles were found and reviewed showing numerous applications of VR/AR in neurosurgery. These applications included their utility as a supplement and augment for neuronavigation in the fields of diagnosis for complex vascular interventions, spine deformity correction, resident training, procedural practice, pain management, and rehabilitation of neurosurgical patients. These technologies have also shown promise in other area of neurosurgery, such as consent taking, training of ancillary personnel, and improving patient comfort during procedures, as well as a tool for training neurosurgeons in other advancements in the field, such as robotic neurosurgery. Conclusions: We present the first review of the immense possibilities of VR in neurosurgery, beyond merely planning for surgical procedures. The importance of VR and AR, especially in “social distancing” in neurosurgery training, for economically disadvantaged sections, for prevention of medicolegal claims and in pain management and rehabilitation, is promising and warrants further research.