Virtual Reality Robot-Assisted SEEG Monitoring Surgery Prototype
Bibliographic record
Abstract
Epilepsy is the most prevalent neurological disorder, afflicting roughly 50 million individuals worldwide. Eighty percent of epilepsy patients reside in low- and middle-income nations, with limited access to expert surgeons and training facilities. Virtual Reality (VR) provides supplementary solutions for developing Stereoelectroencephalography (SEEG) surgery skills, allowing training in highly immersive consumer-level technologies and overcoming the limitations of high-end equipment and patient availability in medical practice. A literature review revealed a trend in developing robot-assisted brain surgery instruments to expose trainees to scenarios otherwise impossible or very difficult to simulate with consumer-grade technology. This paper uses an interdisciplinary approach to develop a robot-assisted VR scenario for SEEG monitoring using the Meta Quest 2 VR headset. Using the System Usability Scale, our preliminary study evaluated the usability and allowed us to identify future enhancements. Our preliminary prototype has allowed us to explore the viability of VR for SEEG monitoring while identifying additional features for customizing the environments, simulating stereo cameras for registering the patient, markers, robot, and surgery room, along with the addition of evaluation and data outputs for assessment.
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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 | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".