Comparative study on the detection of early dental caries using thermo-photonic lock-in imaging and optical coherence tomography
Bibliographic record
Abstract
Early detection of dental caries is known to be the key to the effectiveness of therapeutic and preventive approaches in dentistry. However, existing clinical detection techniques, such as radiographs, are not sufficiently sensitive to detect and monitor the progression of caries at early stages. As such, in recent years, several optics-based imaging modalities have been proposed for the early detection of caries. The majority of these techniques rely on the enhancement of light scattering in early carious lesions, while a few of them are based on the enhancement of light absorption at early caries sites. In this paper, we report on a systemic comparative study on the detection performances of optical coherence tomography (OCT) and thermophotonic lock-in imaging (TPLI) as representative early caries detection modalities based on light scattering and absorption, respectively. Through controlled demineralization studies on extracted human teeth and µCT validation experiments, several detection performance parameters of the two modalities such as detection threshold, sensitivity and specificity have been qualitatively analyzed and discussed. Our experiment results suggests that both modalities have sufficient sensitivity for the detection of well-developed early caries on occlusal and smooth surfaces; however, TPLI provides better sensitivity and detection threshold for detecting very early stages of caries formation, which is deemed to be critical for the effectiveness of therapeutic and preventive approaches in dentistry. Moreover, due to the more specific nature of the light absorption contrast mechanism over light scattering, TPLI exhibits better detection specificity, which results in less false positive readings and thus allows for the proper differentiation of early caries regions from the surrounding intact areas. The major shortcoming of TPLI is its inherent depth-integrated nature, prohibiting the production of depth-resolved/B-mode like images. The outcomes of this research justify the need for a light-absorption based imaging modality with the ability to produce tomographic and depth-resolved images, combining the key advantages of OCT and TPLI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".