Towards early dental caries detection with OCT and polarized Raman spectroscopy
Bibliographic record
Abstract
In recent years, we have been developing optical coherence tomography (OCT) and polarized Raman spectroscopy (PRS) for the detection of early non-cavitated dental caries. OCT provides high resolution morphological depth imaging of incipient caries. With OCT, early lesions can be readily identified as regions of high light backscattering with depth into the enamel as compared to health sound enamel. From the OCT images, the lesion depth can be approximated to provide clinically useful information to guide treatment decisions. In addition, we have derived a parameter known as the optical attenuation coefficient in order to distinguish sound from carious enamel non-subjectively. OCT is being combined with PRS since regions of high light backscattering not related to caries development can lead to false-positive results. PRS provides biochemical specificity along with molecular structural/orientational information. With PRS, the Raman depolarization ratio calculated from the main phosphate vibration at ~959 cm-1 from parallel- and cross-polarized Raman spectra allows discrimination between sound and early developing caries. Early studies on lab bench instrumentation are now being translated into fibre-optic based devices for intra-oral use in patient volunteers for clinical validation. In combination, OCT and PRS have potential for detecting and monitoring early lesions with high sensitivity and high specificity.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".