Assessment of early demineralization in teeth using the signal attenuation in optical coherence tomography images
Bibliographic record
Abstract
Optical coherence tomography imaging is used to improve the detection of incipient carious lesions in dental enamel. Measurements of signal attenuation in images acquired with an 850-nm light source were performed on 21 extracted molars from eight human volunteers. Stronger attenuation was observed for the optical coherence tomography (OCT) signal in healthy enamel than in carious lesions. The measured attenuation coefficients from the two groups form distinct statistical populations. The coefficients obtained from sound enamel fall within the range of 0.70 to 2.14 mm(-1) with a mean value of 1.35 mm(-1), while those in carious regions range from 0.47 to 1.88 mm(-1), with a mean value of 0.77 mm(-1). Three values are selected as the lower threshold for signal attenuation in sound enamel: 0.99, 0.94, and 0.88 mm(-1). These thresholds were selected to provide detection of sound enamel with fixed specificities of 90%, 95%, and 97.5%, respectively. The corresponding sensitivities for the detection of carious lesions are 92.8%, 90.4%, and 87%, respectively, for the sample population used in this study. These findings suggest that attenuation of OCT signal at 850 nm could be an indicator of tooth demineralization and could be used as a marker for early caries detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".