Thermophotonic lock-in imaging of early demineralized and carious lesions in human teeth
Bibliographic record
Abstract
As an extension of frequency-domain photothermal radiometry, a novel dental-imaging modality, thermophotonic lock-in imaging (TPLI), is introduced. This methodology uses photothermal wave principles and is capable of detecting early carious lesions and cracks on occlusal and approximal surfaces as well as early caries induced by artificial demineralizing solutions. The increased light scattering and absorption within early carious lesions increases the thermal-wave amplitude and shifts the thermal-wave centroid, producing contrast between the carious lesion and the intact enamel in both amplitude and phase images. Samples with artificial and natural occlusal and approximal caries were examined in this study. Thermophotonic effective detection depth is controlled by the modulation frequency according to the well-known concept of thermal diffusion length. TPLI phase images are emissivity normalized and therefore insensitive to the presence of stains. Amplitude images, on the other hand, provide integrated information from deeper enamel regions. It is concluded that the results of our noninvasive, noncontacting imaging methodology exhibit higher sensitivity to very early demineralization than dental radiographs and are in agreement with the destructive transverse microradiography mineral density profiles.
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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 | 0.000 | 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".