Ex vivo imaging of early dental caries within the interproximal space
Bibliographic record
Abstract
Optical coherence tomography (OCT) is emerging as a technology that can potentially be used for the detection and monitoring of early dental enamel caries since it can provide high-resolution depth imaging of early lesions. To date, most caries detection optical technologies are well suited for examining caries at facial, lingual, incisal and occlusal surfaces. The approximal surfaces between adjacent teeth are difficult to examine due to lack of visual access and limited space for these new caries detection tools. Using a catheter-style probe developed at the NRC-Industrial Materials Institute, the probe was inserted in to the interproximal space to examine the approximal surfaces with OCT imaging at 1310 nm. The probe was rotated continuously and translated axially to generate depth images in a spiral fashion. The probe was used in a mock tooth arch model consisting of extracted human teeth mounted with dental rope wax in their anatomically correct positions. With this ex vivo model, the probe provided images of the approximal surfaces revealing morphological structural details, regions of calculus, and especially regions of early dental caries (white spot lesions). Results were compared with those obtained from OCT imaging of individual samples where the approximal surfaces of extracted teeth are accessible on a lab-bench. Issues regarding access, regions of interest, and factors to be considered in an in vivo setting will be discussed. Future studies are aimed at using the probe in vivo with patient volunteers.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".