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.

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.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Decoding Intracranial EEG With Machine Learning: A Systematic Review

Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.

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.

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.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

RESULTS

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

CONCLUSIONS

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

O51: ARTIFICIAL INTELLIGENCE UTILIZING RECURRENT NEURAL NETWORKS TO CONTINUOUSLY MONITOR COMPOSITES OF SURGICAL EXPERTISE

Introduction

Many surgical adverse events occur secondary to technical errors related to poor bimanual skills, fatigue and lack of the required expertise. We developed AI algorithms to continuously assess surgical bimanual technical performance during virtual reality simulated surgical tasks. To our knowledge, this is the first attempt in surgery to train AI algorithms to continuously monitor and evaluate bimanual skills comprehensively.

Method

Fifty individuals from four expertise levels (14 experts/neurosurgeons, 14 senior residents, 10 junior residents, 12 medical students) performed two virtual reality simulated surgical tasks with haptic feedback: a subpial tumor resection 5 times and a complex, realistically simulated brain tumor operation once. Each task required complete tumor removal while minimizing bleeding and damage to surrounding tissues using a simulated ultrasonic aspirator and a bipolar. A recurrent neural network continually tracked individual bimanual performance utilizing 16 performance metrics generated every 0.2 seconds.

Result

The recurrent neural network algorithm was successfully trained using neurosurgeons and medical students' data, learning the composites of expertise comparing high and lower skill levels. The trained algorithm outlined and monitored technical skills every 0.2 second continuously organizing performance of each surgical task into three levels: ‘excellent’, ‘average’ and ‘poor’. The percentage time spent on each level was calculated and significant differences found between all four groups for ‘excellent’ and ‘poor’ levels.

Conclusion

AI-powered surgical simulators provide an advanced assessment and training tool. AI's ability to continuous assess bimanual technical skills during surgery may further define the composites necessary to train surgical expertise.

Abbrev

AI: artificial intelligence

Take-home message

By advanced artificial intelligence algorithms surgeon's bi-manual technical skills can be assessed continuously, time periods of poor performance which increase the possibility of errors in performance can be identified.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL RELEVANCE

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

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

OBJECTIVE

Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript.

DESIGN

The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise.

SETTING

This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre.

PARTICIPANTS

The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review.

RESULTS

Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals.

CONCLUSIONS

This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.

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

IMPORTANCE

Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms.

OBJECTIVE

To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure.

DESIGN, SETTING, AND PARTICIPANTS

Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections.

EXPOSURES

All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times.

MAIN OUTCOMES AND MEASURES

Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership.

RESULTS

A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified.

CONCLUSIONS AND RELEVANCE

In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.