Distance sonification in image‐guided neurosurgery
Bibliographic record
Abstract
Image‐guided neurosurgery, or neuronavigation, has been used to visualise the location of a surgical probe by mapping the probe location to pre‐operative models of a patient's anatomy. One common limitation of this approach is that it requires the surgeon to divert their attention away from the patient and towards the neuronavigation system. In order to improve this type of application, the authors designed a system that sonifies (i.e. provides audible feedback of) distance information between a surgical probe and the location of the anatomy of interest. A user study ( n = 15) was completed to determine the utility of sonified distance information within an existing neuronavigation platform (Intraoperative Brain Imaging System (IBIS) Neuronav). The authors’ results were consistent with the idea that combining auditory distance cues with existing visual information from image‐guided surgery systems may result in greater accuracy when locating specified points on a pre‐operative scan, thereby potentially reducing the extent of the required surgical openings, as well as potentially increasing the precision of individual surgical tasks. Further, the authors’ results were also consistent with the hypothesis that combining auditory and visual information reduces the perceived difficulty in locating a target location within a three‐dimensional volume.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".