Ex vivo detection and characterization of early dental caries by optical coherence tomography and Raman spectroscopy
Bibliographic record
Abstract
Early dental caries detection will facilitate implementation of nonsurgical methods for arresting caries progression and promoting tooth remineralization. We present a method that combines optical coherence tomography (OCT) and Raman spectroscopy to provide morphological information and biochemical specificity for detecting and characterizing incipient carious lesions found in extracted human teeth. OCT imaging of tooth samples demonstrated increased light backscattering intensity at sites of carious lesions as compared to the sound enamel. The observed lesion depth on an OCT image was approximately 290 microm matching those previously documented for incipient caries. Using Raman microspectroscopy and fiber-optic-based Raman spectroscopy to characterize the caries further, spectral changes were observed in PO4 (3-) vibrations arising from hydroxyapatite of mineralized tooth tissue. Examination of various ratios of PO4 (3-) nu2, nu3, nu4 vibrations against the nu1 vibration showed consistent increases in carious lesions compared to sound enamel. The changes were attributed to demineralization-induced alterations of enamel crystallite morphology and/or orientation. OCT imaging is useful for screening carious sites and determining lesion depth, with Raman spectroscopy providing biochemical confirmation of caries. The combination has potential for development into a new fiber-optic diagnostic tool enabling dentists to identify early caries lesions with greater sensitivity and 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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".