O6 -methylguanine methyltransferase promoter methylation status of glioblastoma cell line clonal population.

Glioblastoma (GBM) remains a treatment-resistant malignant brain tumor in large part because of its genetic heterogeneity and epigenetic plasticity. In this study, we investigated the epigenetic heterogeneity of GBM by evaluating the methylation status of the O6 -methylguanine methyltransferase (MGMT) promoter in individual clones of a single cell derived from GBM cell lines. The U251 and U373 GBM cell lines, from the Brain Tumour Research Centre of the Montreal Neurological Institute, were used for the experiments. To evaluate the methylation status of the MGMT promoter, pyrosequencing and methylation-specific PCR (MSP) were used. Moreover, mRNA and protein expression levels of MGMT in the individual GBM clones were evaluated. The HeLa cell line, which hyper-expresses MGMT, was used as control. A total of 12 U251 and 12 U373 clones were isolated. The methylation status of 83 of 97 CpG sites in the MGMT promoter were evaluated by pyrosequencing, and 11 methylated CpG sites and 13 unmethylated CpG sites were evaluated by MSP. The methylation status by pyrosequencing was relatively high at CpG sites 3-8, 20-35, and 7-83, in both the U251 and U373 clones. Neither MGMT mRNA nor protein was detected in any clone. These findings demonstrate tumor heterogeneity among individual clones derived from a single GBM cell. MGMT expression may be regulated, not only by methylation of the MGMT promoter but by other factors as well. Further studies are needed to clarify the mechanisms underlying the epigenetic heterogeneity and plasticity of GBM.

From Canadian Surgeon to Chinese Martyr: Dr. Norman Bethune and the Making of a Medical Folk Hero.

This paper reexamines the public memory of Canadian surgeon Norman Bethune. In 1938, Bethune traveled to China to serve at the communist front and to treat soldiers fighting against the invading Japanese army. Throughout China, Bethune is a household name and a communist icon. Back in Canada, however, his name does not evoke the same ubiquity. While Canadians remembered Bethune through biographies, a film, statues, and a small museum, his story in the Anglophone world is confined primarily to the telling of distant history. To explain Bethune's greater notoriety and public presence in China, this essay first turns our attention to Chinese sources that mythologized Bethune's death in 1939. The essay then revisits Chinese propaganda that established Bethune as a lasting political symbol during the Cultural Revolution in the 1960s and 1970s. These national efforts show how a volunteer surgeon such as Bethune became such an important figure in a remote foreign country. China's Communist Party turned Bethune's death into a political event to rally support for their war of resistance against Japan. Later, during the tumultuous period of the Cultural Revolution, Mao Zedong used Bethune to symbolize unwavering service and loyalty to leader and party. This essay utilizes primary materials in McGill's Osler Library and commentary from the field of memory studies to contextualize Bethune and to situate him within the broader narrative of political education that arose in China during the Cultural Revolution. A layered interpretation of Bethune - as doctor, martyr, and symbolic hero - slowly emerges. Political forces in China transformed his memory into legacy and carry this complicated figure into the present day.

Interplay between Rab35 and Arf6 controls cargo recycling to coordinate cell adhesion and migration.

Cells inversely adjust the plasma membrane levels of integrins and cadherins during cell migration and cell-cell adhesion but the regulatory mechanisms that coordinate these trafficking events remain unknown. Here, we demonstrate that the small GTPase Rab35 maintains cadherins at the cell surface to promote cell-cell adhesion. Simultaneously, Rab35 supresses the activity of the GTPase Arf6 to downregulate an Arf6-dependent recycling pathway for ß1-integrin and EGF receptors, resulting in inhibition of cell migration and attenuation of signaling downstream of these receptors. Importantly, the phenotypes of decreased cell adhesion and increased cell migration observed following Rab35 knock down are consistent with the epithelial-mesenchymal transition, a feature of invasive cancer cells, and we show that Rab35 expression is suppressed in a subset of cancers characterized by Arf6 hyperactivity. Our data thus identify a key molecular mechanism that efficiently coordinates the inverse intracellular sorting and cell surface levels of cadherin and integrin receptors for cell migration and differentiation.

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

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

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.

Face, content, and construct validity of a novel VR/AR surgical simulator of a minimally invasive spine operation.

Mixed-reality surgical simulators are seen more objective than conventional training. The simulators' utility in training must be established through validation studies. Establish face-, content-, and construct-validity of a novel mixed-reality surgical simulator developed by McGill University, CAE-Healthcare, and DePuy Synthes. This study, approved by a Research Ethics Board, examined a simulated L4-L5 oblique lateral lumbar interbody fusion (OLLIF) scenario. A 5-point Likert scale questionnaire was used. Chi-square test verified validity consensus. Construct validity investigated 276 surgical performance metrics across three groups, using ANOVA, Welch-ANOVA, or Kruskal-Wallis tests. A post-hoc Dunn's test with a Bonferroni correction was used for further analysis on significant metrics. Musculoskeletal Biomechanics Research Lab, McGill University, Montreal, Canada. DePuy Synthes, Johnson & Johnson Family of Companies, research lab. Thirty-four participants were recruited: spine surgeons, fellows, neurosurgical, and orthopedic residents. Only seven surgeons out of the 34 were recruited in a side-by-side cadaver trial, where participants completed an OLLIF surgery first on a cadaver and then immediately on the simulator. Participants were separated a priori into three groups: post-, senior-, and junior-residents. Post-residents rated validity, median > 3, for 13/20 face-validity and 9/25 content-validity statements. Seven face-validity and 12 content-validity statements were rated neutral. Chi-square test indicated agreeability between group responses. Construct validity found eight metrics with significant differences (p < 0.05) between the three groups. Validity was established. Most face-validity statements were positively rated, with few neutrally rated pertaining to the simulation's graphics. Although fewer content-validity statements were validated, most were rated neutral (only four were negatively rated). The findings underscored the importance of using realistic physics-based forces in surgical simulations. Construct validity demonstrated the simulator's capacity to differentiate surgical expertise.

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.

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.

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.

Bevacizumab use for recurrent high-grade glioma at McGill University Hospital.

BACKGROUND: Bevacizumab, a humanized recombinant anti-vascular endothelial growth factor antibody, was approved in Canada in 2010 for the treatment of high-grade glioma. We report the effectiveness and safety of bevacizumab in the treatment of patients with recurrent high-grade gliomas at a single institution. METHODS: Twenty-seven consecutive patients with high-grade glioma (anaplastic glioma and glioblastoma) at first or subsequent relapse were treated with bevacizumab alone or in combination with chemotherapy. The primary endpoint was progression-free survival (PFS) and secondary endpoints were objective response rate, six month PFS, overall survival (OS), and safety profile. RESULTS: The clinical benefit rate (complete and partial responses plus stable disease) was 59%. Median PFS was 4.3 (95% CI, 3.0-10.9) months, with a six month PFS rate of 43%. Median OS after current relapse was 8.9 (95% CI, 5.8-not reached) months. Ten episodes of grade 3/4 adverse events were observed in nine patients, including fatigue (n = 3), thrombocytopenia (n = 4), and myelotoxicity, febrile neutropenia, and pulmonary embolism (each n = 1). CONCLUSIONS: We consider the efficacy and safety profile of bevacizumab is comparable to other cohorts of patients treated for recurrent high-grade glioma at other international institutions.

Performance in a Simulated Virtual Reality Anterior Cervical Discectomy and Fusion Task: Disc Residual, Rate of Removal, and Efficiency Analyses.

BACKGROUND AND OBJECTIVES: Anterior cervical discectomy and fusion (ACDF) is among the most common spine procedures. The Sim-Ortho virtual reality simulator platform contains a validated ACDF simulated task for performance assessment. This study aims to develop a methodology to extract three-dimensional data and reconstruct and quantitate specific simulated disc tissues to generate novel metrics to analyze performance metrics of skilled and less skilled participants. METHODS: We used open-source platforms to develop a methodology to extract three-dimensional information from ACDF simulation data. Metrics generated included, efficiency index, disc volumes removed from defined regions, and rate of tissue removal from superficial, central, and deep disc regions. A pilot study was performed to assess the utility of this methodology to assess expertise during the ACDF simulated procedure. RESULTS: The system outlined, extracts data allowing the development of a methodology which accurately reconstructs and quantitates 3-dimensional disc volumes. In the pilot study, data sets from 27 participants, divided into postresident, resident, and medical student groups, allowed assessment of multiple novel metrics, including efficiency index (surgical time spent in actively removing disc), where the postresident group spent 61.8% of their time compared with 53% and 30.2% for the resident and medical student groups, respectively ( P = .01). During the annulotomy component, the postresident group removed 47.4% more disc than the resident groups and 102% more than the medical student groups ( P = .03). CONCLUSION: The methodology developed in this study generates novel surgical procedural metrics from 3-dimensional data generated by virtual reality simulators and can be used to assess surgical performance.

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.

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.

Suicidal ideation and attempts in brain tumor patients and survivors: A systematic review.

Background: Subsequent to a diagnosis of a brain tumor, psychological distress has been associated with negative effects on mental health as well as suicidality. The magnitude of such impact has been understudied in the literature. We conducted a systematic review to examine the impact of a brain tumor on suicidality (both ideation and attempts). Methods: In accordance with the PRISMA guidelines, we searched for relevant peer-reviewed journal articles on PubMed, Scopus, and Web of Science databases from inception to October 20, 2022. Studies investigating suicide ideation and/or attempt among patients with brain tumors were included. Results: Our search yielded 1,998 articles which were screened for eligibility. Seven studies consisting of 204,260 patients were included in the final review. Four studies comprising 203,906 patients (99.8%) reported elevated suicidal ideation and suicide attempt incidence compared with the general population. Prevalence of ideation and attempts ranged from 6.0% to 21.5% and 0.03% to 3.33%, respectively. Anxiety, depression, pain severity, physical impairment, glioblastoma diagnosis, male sex, and older age emerged as the primary risk factors associated with increased risk of suicidal ideation and attempts. Conclusion: Suicidal ideation and attempts are increased in patients and survivors of brain tumors compared to the general population. Early identification of patients exhibiting these behaviors is crucial for providing timely psychiatric support in neuro-oncological settings to mitigate potential harm. Future research is required to understand pharmacological, neurobiological, and psychiatric mechanisms that predispose brain tumor patients to suicidality.

Online database of clinical MR and ultrasound images of brain tumors.

PURPOSE: One of the important challenges in the field of medical imaging is finding real clinical images with which to validate new image processing algorithms. This is particularly true for tracked 3D ultrasound images of the brain. METHODS: In 2010, pre- and postoperative magnetic resonance and intraoperative ultrasound images were acquired from brain tumor patients involved in the authors' imaging study at the Montreal Neurological Institute. RESULTS: These data are available online at the Montreal Neurological Institute's Brain Images of Tumors for Evaluation database, termed here the MNI BITE database. It contains ultrasound and magnetic resonance images from 14 patients. Each patient underwent a preoperative and a postoperative T1-weighted magnetic resonance scan with gadolinium enhancement, and multiple intraoperative B-mode images were acquired before and after resection. Corresponding features were manually selected in some image pairs for validation. All images are in MINC format, the file format used at the authors' institute for image processing. The MINC tools are available for free download at packages.bic.mni.mcgill.ca. CONCLUSIONS: This is the first online database of its kind. These images can be used by image processing scientists as well as clinicians wishing to compare findings from magnetic resonance and ultrasound imaging.

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.

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.

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.