Bibliographic record
Abstract
Purpose: Tracked navigation systems require large carts of equipment, specialized technicians, and are impractical in bedside neurosurgical procedures. For bedside procedures like an opening of the skull for removing pressure caused by internal bleeding, navigation could improve the accuracy of the drill placement. We use the Microsoft HoloLens to display a hologram floating in the patient’s head to mark a drilling location on the skull. The accuracy of this placement is assessed to determine the feasibility of using the HoloLens to mark a drilling location within a clinically acceptable range. Methods: A 3D model of the head is created from CT scans and imported to the HoloLens. The hologram is interactively registered to the patient and the drilling location is marked on the skull (Figure 1). 3DSlicer, Unity, and Visual Studio were used for implementing the software. The system was tested by 7 users. They each performed 6 registrations on phantoms with markers placed at 3 plausible drilling locations. Registration accuracy was determined by measuring the distance between the holographic and physical markers. Results: Users placed 98% of the markers within the clinically acceptable range of 10 mm in an average time of 4:46 min. Conclusion: It is feasible to mark a neurosurgical drilling location with clinically acceptable accuracy using the Microsoft HoloLens, within an acceptable length of time. This technology may also prove useful for procedures that require higher accuracy of location and drain trajectory such as the placement of external ventricular drains.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".