Preliminary Study on the Clinical Application of Augmented Reality Neuronavigation
Bibliographic record
Abstract
OBJECTIVE: To develop an augmented reality (AR) neuronavigation system with Web cameras and examine its clinical utility. METHODS: The utility of the system was evaluated in three patients with brain tumors. One patient had a glioblastoma and two patients had convexity meningiomas. Our navigation system comprised the open-source software 3D Slicer (Brigham and Women's Hospital, Boston, Massachusetts, USA), the infrared optical tracking sensor Polaris (Northern Digital Inc., Waterloo, Canada), and Web cameras. We prepared two different types of Web cameras: a handheld type and a headband type. Optical markers were attached to each Web camera. We used this system for skin incision planning before the operation, during craniotomy, and after dural incision. RESULTS: We were able to overlay these images in all cases. In Case 1, accuracy could not be evaluated because the tumor was not on the surface, though it was generally suitable for the outline of the external ear and the skin. In Cases 2 and 3, the augmented reality error was ∼2 to 3 mm. CONCLUSION: AR technology was examined with Web cameras in neurosurgical operations. Our results suggest that this technology is clinically useful in neurosurgical procedures, particularly for brain tumors close to the brain surface.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".