PreVISE: an efficient virtual reality system for SEEG surgical planning
Bibliographic record
Abstract
Epilepsy is a neurological disorder characterized by recurring seizures that can cause a wide range of symptoms. Stereo-electroencephalography (SEEG) is a diagnostic procedure where multiple electrodes are stereotactically implanted within predefined brain regions to identify the seizure onset zone, which needs to be surgically removed or disconnected to achieve remission of focal epilepsy. This procedure is complex and challenging due to two main reasons. First, as electrode placement requires good accuracy in desired brain regions, excellent knowledge and understanding of the 3D brain anatomy is required. Second, as typically multiple SEEG electrodes need to be implanted, the positioning of intracerebral electrodes must avoid critical structures (e.g., blood vessels) to ensure patient safety. Traditional SEEG surgical planning relies on 2D display of multi-contrast volumetric medical imaging data, and places a high cognitive demand for surgeons' spatial understanding, resulting in potentially sub-optimal surgical plans and extensive planning time (~ 15 min per electrode). In contrast, virtual reality (VR) presents an intuitive and immersive approach that can offer more intuitive visualization of 3D data as well as potentially enhanced efficiency for neurosurgical planning. Unfortunately, existing VR systems for SEEG surgery only focus on the visualization of post-surgical scans to confirm electrode placement. To address the need, we introduce the first VR system for SEEG planning that integrates user-friendly and efficient visualization and interaction strategies while providing real-time feedback metrics, including distances to nearest blood vessels, angles of insertion, and the overall surgical quality scores. The system reduces the surgical planning time by 91%.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".