Towards a bimodal proximity sensor for in situ neurovascular bundle detection during dental implant surgery
Bibliographic record
Abstract
Proof of concept results are presented towards an in situ bimodal proximity sensor for neurovascular bundle detection during dental implant surgery using combined near infrared absorption (NIR) and optical coherence tomography (OCT) techniques. These modalities are shown to have different sensitivity to the proximity of optical contrast from neurovascular bundles. NIR AC and DC signals from the pulsing of an artery enable qualitative ranging of the bundle in the millimeter range, with best sensitivity around 0.5-3mm distance in a custom phantom setup. OCT provides structural mapping of the neurovascular bundle at sub-millimeter distances in an ex vivo human jaw bone. Combining the two techniques suggests a novel ranging system for the surgeon that could be implemented in a "smart drill." The proximity to the neurovascular bundle can be tracked in real time in the range of a few millimeters with NIR signals, after which higher resolution imaging OCT to provide finer ranging in the sub-millimeter distances.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".