First step toward translation of thermophotonic lock-in imaging to dentistry as an early caries detection technology
Bibliographic record
Abstract
Early detection of the most prevalent oral disease worldwide, i.e., dental caries, still remains as one of the major challenges in dentistry. The current dental standard of care relies on caries detection methods, such as visual inspection and x-ray radiography, which lack the sufficient specificity and sensitivity to detect caries at early stages of formation when they can be healed. We report on the feasibility of early caries detection in a clinically and commercially viable thermophotonic imaging system. The system incorporates intensity-modulated laser light along with a low-cost long-wavelength infrared (LWIR; 8 to 14???m) camera, providing diagnostic contrast based on the enhanced light absorption of early caries. The LWIR camera is highly suitable for integration into clinical platforms because of its low weight and cost. In addition, through theoretical modeling, we show that LWIR detection enhances the diagnostic contrast due to the minimal LWIR transmittance of enamel and suppression of the masking effect of the direct thermal Planck emission. Diagnostic performance of the system and its detection threshold are experimentally evaluated by monitoring the inception and progression of artificially induced occlusal and smooth surface caries. The results are suggestive of the suitability of the developed LWIR system for detecting early dental caries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".