Neurosurgical burr hole placement using the Microsoft HoloLens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Tracked navigation systems are generally impractical in bedside neurosurgical procedures, such as a twist-drill crainiostomy for the removal of a subdural hematoma, where the use of navigation could optimize the placement of the drill in relation to the underlying fluid. We use the Microsoft HoloLens to display a hologram floating in the patient’s head to mark a burr hole on the skull. METHODS: A 3D model of the head, hematoma and burr hole is created from CT and imported to the HoloLens. The hologram is interactively registered to the patient and the burr hole is marked on the skull. 3D Slicer, Unity, and Visual Studio were used for software development. The system was tested by 6 inexperienced and 1 experienced users. They each performed 6 registrations on phantoms with fiducial markers placed at 3 plausible burr hole locations on each side of the head. Registration accuracy was determined by measuring the distance between the holographic and physical markers. RESULTS: Inexperienced users placed 98% of the markers within the clinically acceptable range of 10 mm in an average time of 4:46 min. The experienced user placed 100% of the markers within the acceptable range in an average time of 2:52 min. CONCLUSION: It is feasible to mark a neurosurgical burr hole 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 drill location and drain trajectory such as the placement of external ventricular drains.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it