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

Cancer patients' acceptance of virtual reality interventions for self-emotion regulation

This study investigates the acceptability of Virtual reality (VR) technology for emotional regulation among cancer patients. Drawing from extensive literature, we enhanced external variables across user characteristics, product impact factors, and social environment influences, creating the "Theoretical Model of Cancer Patients' Acceptance of VR Intervention for Self-Emotion Regulation." Surveying 489 Chinese cancer patients validated the model's strong reliability through SPSS AMOS analysis. The acceptance of VR intervention for self-emotional regulation among cancer patients was assessed, revealing that the average scores across all 13 dimensions exceeded 3. This indicates that cancer patients hold a positive attitude toward VR-based emotional regulation interventions. Perceived usefulness, usage attitude, social norms, immersion, and personal innovation correlated positively with behavioral intention, while technological anxiety and perceived risk showed negative correlations. Findings support 15 hypotheses, offering theoretical backing for VR technology in emotional regulation for cancer patients. These insights provide medical institutions with valuable data on patient attitudes, facilitating the development of targeted treatment approaches.

The Effectiveness of Immersive Virtual Reality Simulation as an Innovative Learning Strategy for Acquisition of Clinical Skills in Nursing Education: Experimental Design

Background: A transformation of learning in nursing is necessary to prepare students for developing complex clinical environments. The essential aim of clinical nursing learning is to enhance the integration of theoretical knowledge in the clinical environment by using various innovative strategies, such as immersive virtual reality (VR) simulation to develop a learning process that allows students to gain knowledge and perform skills in a visually attractive way, which enhances the quality and safety of clinical learning through repeated exposure to educational content that supports students' cognitive and psychomotor skills. Objective: This study was aimed at determining the effectiveness of immersive VR simulation as a learning strategy on the acquisition of intramuscular injection skills in nursing education and the performance level of nursing students compared with a physical learning environment (low-fidelity simulation). Materials and Metods: The experimental design (pre-post-test) was used among first-year nursing students (N = 66) (control group = 33, hip model and experimental group = 33, VR simulation) of the summer semester of 2019-2020 in the Faculty of Nursing at Near East University in Cyprus. Results: There is a significant difference between both groups in performance psychomotor skills scores, and the mean was higher in the experimental group (P = 0.002) and a significantly longer period of time than in the control group (P < 0.05). Conclusion: Immersive VR simulation is a supplementary tool and useful teaching-learning strategy for training in nursing education alongside physical laboratory (hip-model and mannequin) and psychomotor skills requiring the ordering of skill steps in teaching, and it provides realistic experiences in a safe environment instead of the unavailability of actual customers in clinical settings.

The Future of Sustainable Neurosurgery: Is a Moonshot Plan for Artificial Intelligence and Robot-Assisted Surgery Possible in Japan?

Japanese neurosurgery faces challenges such as a declining number of neurosurgeons and their concentration in urban areas. Particularly in rural areas, access to neurosurgical care for patients with conditions, such as stroke, is limited, raising concerns about the collapse of regional healthcare. Robot-assisted surgical technologies have advanced in recent years, contributing to the improved precision and safety of deep brain surgery. This study proposes the "Artificial Intelligence (AI) and Robot-Assisted Surgery Moonshot Plan" for Japan, comprising 5 pillars: 1) establishment of regional medical centers, 2) development of remote surgery systems, 3) enhancement of robotic-assisted surgery training programs, 4) integration of AI technologies, and 5) promotion of industry-academia-government collaboration. In addition, strengthening the approach to spinal surgery is expected to revitalize regional medical centers, optimize the number of neurosurgeons, improve surgical skills, and promote minimally invasive surgery. This study analyzed the current status and challenges of Japanese neurosurgery through a literature review and statistical analysis. AI is used in various aspects of neurosurgery, including diagnostic support, surgical planning and navigation, treatment outcome prediction, intraoperative monitoring, robot-assisted surgery, and rehabilitation. However, challenges, such as data bias, ethical issues, costs, and regulations, remain. In Japan, issues such as the uneven distribution and decline of neurosurgeons, collapse of regional healthcare, and increase in the number of patients with spinal disorders due to aging have been highlighted. The "AI and Robot-Assisted Surgery Moonshot Plan" serves as a guide to overcome the challenges of neurosurgery in Japan and establish a sustainable medical system.

Intraoperative interaction modeling between surgical instruments and soft tissues in neurosurgery based on energy functions

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas. Experimental results show that the proposed method meets the requirements of neurosurgical simulation.

Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial

Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer. This randomized controlled trial compared real-time AI feedback to in-person expert instruction. Ninety-seven medical trainees completed a 90-min simulation training with five practice tumor resections followed by a realistic brain tumor resection. They were randomly assigned into 1-real-time AI feedback, 2-in-person expert instruction, and 3-no real-time feedback. Performance was assessed using a composite-score and Objective Structured Assessment of Technical Skills rating, rated by blinded experts. Training with real-time AI feedback (n = 33) resulted in significantly better performance outcomes compared to no real-time feedback (n = 32) and in-person instruction (n = 32), .266, [95% CI .107 .425], p < .001; .332, [95% CI .173 .491], p = .005, respectively. Learning from AI resulted in similar OSATS ratings (4.30 vs 4.11, p = 1) compared to in-person training with expert instruction. Intelligent systems may refine the way operating skills are taught, providing tailored, quantifiable feedback and actionable instructions in real-time.

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.

Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial

OBJECTIVE

To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections.

HYPOTHESES

(1) Providing feedback results in better learning outcomes in teaching surgical technical skill when compared to practice alone with no tailored performance feedback. (2) Providing more visual and visuospatial feedback results in better learning outcomes when compared to providing numerical feedback.

DESIGN

A prospective 4-parallel-arm randomized controlled trial.

SETTING

Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Canada.

PARTICIPANTS

Medical students (n = 120) from 4 Quebec medical schools.

RESULTS

Participants completed a virtually simulated tumor resection task 5 times while receiving 1 of 4 feedback based on their group allocation: (1) practice-alone without feedback, (2) numerical feedback, (3) visual feedback, and (4) visuospatial feedback. Outcome measures were participants' scores on 14-performance metrics and the number of expert benchmarks achieved during each task. There were no significant differences in the first task which determined baseline performance. A statistically significant interaction between feedback allocation and task repetition was found on the number of benchmarks achieved, F (10.558, 408.257)=3.220, p < 0.001. Participants in all feedback groups significantly improved their performance compared to baseline. The visual feedback group achieved significantly higher number of benchmarks than the practice-alone group by the third repetition of the task, p = 0.005, 95%CI [0.42 3.25]. Visual feedback and visuospatial feedback improved performance significantly by the second repetition of the task, p = 0.016, 95%CI [0.19 2.71] and p = 0.003, 95%CI [0.4 2.57], respectively.

CONCLUSION

Simulations with autonomous visual computer assistance may be effective pedagogical tools in teaching bimanual operative skills via visual and visuospatial feedback information delivery.

Deep Learning: A Primer for Neurosurgeons

This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning architectures, including convolutional and recurrent neural networks, and their applications in analyzing neuroimaging data for brain tumors, spinal cord injuries, and other neurological conditions. The integration of DL in neurosurgical robotics and the potential for fully autonomous surgical procedures are discussed, highlighting advancements in surgical precision and patient outcomes. The chapter also examines the challenges of data privacy, quality, and interpretability that accompany the implementation of DL in neurosurgery. The potential for DL to revolutionize neurosurgical practices through improved diagnostics, patient-specific surgical planning, and the advent of intelligent surgical robots is underscored, promising a future where technology and healthcare converge to offer unprecedented solutions in neurosurgery.

AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training

Importance

To better elucidate the role of artificial intelligence (AI) in surgical skills training requires investigations in the potential existence of a hidden curriculum.

Objective

To assess the pedagogical value of AI-selected technical competencies and their extended effects in surgical simulation training.

Design, Setting, and Participants

This cohort study was a follow-up of a randomized clinical trial conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at the Montreal Neurological Institute, McGill University, Montreal, Canada. Surgical performance metrics of medical students exposed to an AI-enhanced training curriculum were compared with a control group of participants who received no feedback and with expert benchmarks. Cross-sectional data were collected from January to April 2021 from medical students and from March 2015 to May 2016 from experts. This follow-up secondary analysis was conducted from June to September 2022. Participants included medical students (undergraduate year 0-2) in the intervention cohorts and neurosurgeons to establish expertise benchmarks.

Exposure

Performance assessment and personalized feedback by an intelligent tutor on 4 AI-selected learning objectives during simulation training.

Main Outcomes and Measures

Outcomes of interest were unintended performance outcomes, measured by significant within-participant difference from baseline in 270 performance metrics in the intervention cohort that was not observed in the control cohort.

Results

A total of 46 medical students (median [range] age, 22 [18-27] years; 27 [59%] women) and 14 surgeons (median [range] age, 45 [35-59] years; 14 [100%] men) were included in this study, and no participant was lost to follow-up. Feedback on 4 AI-selected technical competencies was associated with additional performance change in 32 metrics over the entire procedure and 20 metrics during tumor removal that was not observed in the control group. Participants exposed to the AI-enhanced curriculum demonstrated significant improvement in safety metrics, such as reducing the rate of healthy tissue removal (mean difference, -7.05 × 10-5 [95% CI, -1.09 × 10-4 to -3.14 × 10-5] mm3 per 20 ms; P < .001) and maintaining a focused bimanual control of the operative field (mean difference in maximum instrument divergence, -4.99 [95% CI, -8.48 to -1.49] mm, P = .006) compared with the control group. However, negative unintended effects were also observed. These included a significantly lower velocity and acceleration in the dominant hand (velocity: mean difference, -0.13 [95% CI, -0.17 to -0.09] mm per 20 ms; P < .001; acceleration: mean difference, -2.25 × 10-2 [95% CI, -3.20 × 10-2 to -1.31 × 10-2] mm per 20 ms2; P < .001) and a significant reduction in the rate of tumor removal (mean difference, -4.85 × 10-5 [95% CI, -7.22 × 10-5 to -2.48 × 10-5] mm3 per 20 ms; P < .001) compared with control. These unintended outcomes diverged students' movement and efficiency performance metrics away from the expertise benchmarks.

Conclusions and Relevance

In this cohort study of medical students, an AI-enhanced curriculum for bimanual surgical skills resulted in unintended changes that improved performance in safety but negatively affected some efficiency metrics. Incorporating AI in course design requires ongoing assessment to maintain transparency and foster evidence-based learning objectives.

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.

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.

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.

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.

See One, Do One, Share One - Introducing Visual Abstracts in Journal Publication

The visual abstract, read with ease and speed, is a logical evolution for today's journals to attract and maintain readers. However, many faculty have not yet constructed visual abstracts. This manuscript is a means to consolidate theory and commentaries into a cohesive explanation of why and how to develop a visual abstract. Tremendous growth opportunity exists for this innovation in the medical landscape, with current and future applications in journal publications, summary and dispersal of practice guidelines, and delivery of educational and training materials.

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.

Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial

IMPORTANCE

To better understand the emerging role of artificial intelligence (AI) in surgical training, efficacy of AI tutoring systems, such as the Virtual Operative Assistant (VOA), must be tested and compared with conventional approaches.

OBJECTIVE

To determine how VOA and remote expert instruction compare in learners' skill acquisition, affective, and cognitive outcomes during surgical simulation training.

DESIGN, SETTING, AND PARTICIPANTS

This instructor-blinded randomized clinical trial included medical students (undergraduate years 0-2) from 4 institutions in Canada during a single simulation training at McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal, Canada. Cross-sectional data were collected from January to April 2021. Analysis was conducted based on intention-to-treat. Data were analyzed from April to June 2021.

INTERVENTIONS

The interventions included 5 feedback sessions, 5 minutes each, during a single 75-minute training, including 5 practice sessions followed by 1 realistic virtual reality brain tumor resection. The 3 intervention arms included 2 treatment groups, AI audiovisual metric-based feedback (VOA group) and synchronous verbal scripted debriefing and instruction from a remote expert (instructor group), and a control group that received no feedback.

MAIN OUTCOMES AND MEASURES

The coprimary outcomes were change in procedural performance, quantified as Expertise Score by a validated assessment algorithm (Intelligent Continuous Expertise Monitoring System [ICEMS]; range, -1.00 to 1.00) for each practice resection, and learning and retention, measured from performance in realistic resections by ICEMS and blinded Objective Structured Assessment of Technical Skills (OSATS; range 1-7). Secondary outcomes included strength of emotions before, during, and after the intervention and cognitive load after intervention, measured in self-reports.

RESULTS

A total of 70 medical students (41 [59%] women and 29 [41%] men; mean [SD] age, 21.8 [2.3] years) from 4 institutions were randomized, including 23 students in the VOA group, 24 students in the instructor group, and 23 students in the control group. All participants were included in the final analysis. ICEMS assessed 350 practice resections, and ICEMS and OSATS evaluated 70 realistic resections. VOA significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group (P < .001). Realistic Expertise Scores were significantly higher for the VOA group compared with instructor (mean difference, 0.53 [95% CI, 0.40 to 0.67] points; P < .001) and control (mean difference. 0.49 [95% CI, 0.34 to 0.61] points; P < .001) groups. Mean global OSATS ratings were not statistically significant among the VOA (4.63 [95% CI, 4.06 to 5.20] points), instructor (4.40 [95% CI, 3.88-4.91] points), and control (3.86 [95% CI, 3.44 to 4.27] points) groups. However, on the OSATS subscores, VOA significantly enhanced the mean OSATS overall subscore compared with the control group (mean difference, 1.04 [95% CI, 0.13 to 1.96] points; P = .02), whereas expert instruction significantly improved OSATS subscores for instrument handling vs control (mean difference, 1.18 [95% CI, 0.22 to 2.14]; P = .01). No significant differences in cognitive load, positive activating, and negative emotions were found.

CONCLUSIONS AND RELEVANCE

In this randomized clinical trial, VOA feedback demonstrated superior performance outcome and skill transfer, with equivalent OSATS ratings and cognitive and emotional responses compared with remote expert instruction, indicating advantages for its use in simulation training.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04700384.

Virtual Reality Simulator Enhances Ergonomics Skills for Neurosurgeons

This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The project main objective is to investigate the ability virtual reality to enhance the training.An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is two fold. From one side investigates the new requirements for the enhancement of Neurosurgenos’ training and adoption on Virtual Reality Simulator. From the other side contributes to the body of knowledge related to the required Ergonomics skills.

Best Practices Using Ex Vivo Animal Brain Models in Neurosurgical Education to Assess Surgical Expertise

BACKGROUND

Ex vivo animal brain simulation models are being increasingly used for neurosurgical training because these models can replicate human brain conditions. The goal of the present report is to provide the neurosurgical community interested in using ex vivo animal brain simulation models with guidelines for comprehensively and rigorously conducting, documenting, and assessing this type of research.

METHODS

In consultation with an interdisciplinary group of physicians and researchers involved in ex vivo models and a review of the literature on the best practices guidelines for simulation research, we developed the "ex vivo brain model to assess surgical expertise" (EVBMASE) checklist. The EVBMASE checklist provides a comprehensive quantitative framework for analyzing and reporting studies involving these models. We applied The EVBMASE checklist to the studies reported of ex vivo animal brain models to document how current ex vivo brain simulation models are used to train surgical expertise.

RESULTS

The EVBMASE checklist includes defined subsections and a total score of 20, which can help investigators improve studies and provide readers with techniques to better assess the quality and any deficiencies of the research. We classified 18 published ex vivo brain models into modified (group 1) and nonmodified (group 2) models. The mean total EVBMASE score was 11 (55%) for group 1 and 4.8 (24.2%) for group 2, a statistically significant difference (P = 0.006) mainly attributed to differences in the simulation study design section (P = 0.003).

CONCLUSIONS

The present findings should help contribute to more rigorous application, documentation, and assessment of ex vivo brain simulation research.

Minimally Invasive Subfrontal Approach: How to Make it Safe and Effective from the Olfactory Groove to the Mesial Temporal Lobe

BACKGROUND

 Different surgical approaches have been developed to manage lesions of the anterior and middle skull base areas. Frontal, pterional, bifrontal, and fronto-orbito-zygomatic approaches are traditionally used to reach these regions. With advancements in the neurosurgical field, skull opening should be simple and as minimally invasive as possible, tailored on the surgical corridor to the target. The supraorbital approach and the "keyhole" concept have been introduced and popularized by Axel Perneczky starting from 1998 and are now considered a part of everyday practice. The extended possibilities of this surgical route, considering the reachable targets and surgical limits, are described and systematically analyzed, including a description of the salient surgical anatomy, presenting different illustrative cases.

METHODS AND RESULTS

 Different illustrative cases are presented and discussed to underline the potentials and limits of the minimally invasive subfrontal approach (MISFA) and the possibilities to tailoring the craniotomy on the basis of the targets: extra-axial lesions with different localizations (anterior roof of the orbit, olfactory groove, tuberculum sellae, medial third of the sphenoid wing, anterior and posterior clinoid process), deeper intra-axial lesions (gyrus rectus, medial temporal lobe-uncus-amygdala-anterior hippocampus), and vascular lesions (anterior communicating aneurysm). Each case has been preoperatively planned considering the anatomical and radiologic features and using virtual simulation software to tailor the best possible corridor to reach the surgical target.

CONCLUSIONS

 The MISFA is a safe multicorridor approach that can be used efficiently to manage lesions of the anterior and middle skull base areas with extremely low approach-related morbidity.

Efficacy of a virtual reality-based basic and clinical fused curriculum for clinical education on the lumbar intervertebral disc

OBJECTIVE

Today, minimally invasive procedures have become mainstream surgical procedures. Percutaneous endoscopic transforaminal discectomy for lumbar disc herniation (LDH) requires profound knowledge of the laparoscopic lumbar anatomy. Immersive virtual reality (VR) provides three-dimensional patient-specific models to help in the process of preclinical surgical preparation. In this study, the authors investigated the efficacy of VR application in LDH for training orthopedic residents and postgraduates.

METHODS

VR images of the lumbar anatomy were created with immersive VR and mAnatomy software. The study was conducted among 60 residents and postgraduates. A questionnaire was developed to assess the effect of and satisfaction with this VR-based basic and clinical fused curriculum. The teaching effect was also evaluated through a postlecture test, and the results of the prelecture surgical examination were taken as baselines.

RESULTS

All participants in the VR group agreed that VR-based education is practical, attractive, and easy to operate, compared to traditional teaching, and promotes better understanding of the anatomical structures involved in LDH. Learners in the VR group achieved higher scores on an anatomical and clinical fusion test than learners in the traditional group (84.67 ± 14.56 vs 76.00 ± 16.10, p < 0.05).

CONCLUSIONS

An immersive VR-based basic and clinical fused curriculum can increase residents' and postgraduates' interest and support them in mastering the structural changes and complicated symptoms of LDH. However, a simplified operational process and more realistic haptics of the VR system are necessary for further surgical preparation and application.

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.

The role of virtual reality in the changing landscape of surgical training

BACKGROUND

The current coronavirus disease 2019 pandemic has caused unprecedented challenges to surgical training across the world. With the widespread cancellations of clinical and academic activities, educators are looking to technological advancements to help 'bridge the gap' and continue medical education.

SOLUTIONS

Simulation-based training as the 'gold standard' for medical education has limitations that prevent widespread adoption outside suitably resourced centres. Virtual reality has the potential to surmount these barriers, whilst fulfilling the fundamental aim of simulation-based training to provide a safe, effective and realistic learning environment.

CURRENT LIMITATIONS AND INSIGHTS FOR FUTURE

The main limitations of virtual reality technology include comfort and the restrictive power of mobile processors. There exists a clear developmental path to address these restrictions. Continued developments of the hardware and software set to deepen immersion and widen the possibilities within surgical education.

CONCLUSION

In the post coronavirus disease 2019 educational landscape, virtual, augmented and mixed reality technology may prove invaluable in the training of the next generation of surgeons.