The Development of a Virtual Simulator for Training Neurosurgeons to Perform and Perfect Endoscopic Endonasal Transsphenoidal Surgery
Bibliographic record
Abstract
BACKGROUND: A virtual reality (VR) neurosurgical simulator with haptic feedback may provide the best model for training and perfecting surgical techniques for transsphenoidal approaches to the sella turcica and cranial base. Currently there are 2 commercially available simulators: NeuroTouch (Cranio and Endo) developed by the National Research Council of Canada in collaboration with surgeons at teaching hospitals in Canada, and the Immersive Touch. Work in progress on other simulators at additional institutions is currently unpublished. OBJECTIVE: This article describes a newly developed application of the NeuroTouch simulator that facilitates the performance and assessment of technical skills for endoscopic endonasal transsphenoidal surgical procedures as well as plans for collecting metrics during its early use. METHODS: The main components of the NeuroTouch-Endo VR neurosurgical simulator are a stereovision system, bimanual haptic tool manipulators, and high-end computers. The software engine continues to evolve, allowing additional surgical tasks to be performed in the VR environment. Device utility for efficient practice and performance metrics continue to be developed by its originators in collaboration with neurosurgeons at several teaching hospitals in the United States. Training tasks are being developed for teaching 1- and 2-nostril endonasal transsphenoidal approaches. Practice sessions benefit from anatomic labeling of normal structures along the surgical approach and inclusion (for avoidance) of critical structures, such as the internal carotid arteries and optic nerves. CONCLUSION: The simulation software for NeuroTouch-Endo VR simulation of transsphenoidal surgery provides an opportunity for beta testing, validation, and evaluation of performance metrics for use in neurosurgical residency training. ABBREVIATIONS: CTA, cognitive task analysisVR, virtual reality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".