Diagnosis of Pit and Fissure Caries Using Frequency-Domain Infrared Photothermal Radiometry and Modulated Laser Luminescence
Bibliographic record
Abstract
Non-intrusive, non-contacting frequency-domain photothermal radiometry (FD-PTR or PTR) and frequency-domain luminescence (FD-LUM or LUM) have been used with 659- and 830-nm laser sources to assess the pits and fissures on the occlusal surfaces of human teeth. Fifty-two human teeth were examined with simultaneous measurements of PTR and LUM and were compared to conventional diagnostic methods including continuous (dc) luminescence (DIAGNOdent), visual inspection and radiographs. To compare each method to the others, sensitivities and specificities were calculated by using histological observations as the gold standard. With the combined criteria of four PTR and LUM signals (two amplitudes and two phases), it was found that the sensitivity of this method was much higher than any of the other methods used in this study, whereas the specificity was comparable to that of dc luminescence diagnostics. Therefore, PTR and LUM, as a combined technique, has the potential to be a reliable tool to diagnose early pit and fissure caries and could provide detailed information about deep lesions. Using the longer wavelength (830-nm) laser source, it has been shown that detection of deeper subsurface lesions than the 659-nm probe provides is possible.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".