High-Fidelity Virtual Reality Simulation for the Middle Cranial Fossa Approach—Modules for Surgical Rehearsal and Education
Bibliographic record
Abstract
BACKGROUND: Virtual reality simulation has gained prominence as a valuable surgical rehearsal and education tool in neurosurgery. Approaches to the internal auditory canal, cerebellopontine angle, and ventral brainstem region using the middle cranial fossa are not well explored by simulation. OBJECTIVE: We hope to contribute to this paucity in simulation tools devoted to the lateral skull base, specifically the middle cranial fossa approach. METHODS: Eight high-resolution microcomputed tomography scans of human cadavers were used as volumetric data sets to construct a high-fidelity visual and haptic rendering of the middle cranial fossa using CardinalSim software. Critical neurovascular structures related to this region of the skull base were segmented and incorporated into the modules. RESULTS: The virtual models illustrate the 3-dimensional anatomic relationships of neurovascular structures in the middle cranial fossa and allow a realistic interactive drilling environment. This is facilitated by the ability to render bone opaque or transparent to reveal the proximity to critical anatomy allowing for practice of the virtual dissection in a graduated fashion. CONCLUSION: We have developed a virtual library of middle cranial fossa approach models, which integrate relevant neurovascular structures with aims to improve surgical training and education. A ready extension is the potential for patient-specific application and pathology.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".