Development of an in vitro protocol to induce artificial white spot lesions and their characterization using optical coherence tomography and micro CT
Bibliographic record
Abstract
Abstract Background White spot lesions (WSL) represent the earliest stage of caries formation in which mineral is lost from the enamel surface, but the surface retains its integrity. At this stage, remineralization of enamel is generally considered possible. This study aimed to develop a reliable in vitro protocol for the creation of artificially induced WSL and to examine the WSL by micro-computed tomography (microCT) and optical coherence tomography (OCT). Methods Artificial WSL lesions were created by immersing human molars in a lactic acid solution under constant agitation at 37ºC for seven days. MicroCT and OCT were used to image the lesions before comparing them to naturally occurring WSL. In addition, the mineral density of the demineralized enamel and the depth of the lesion was characterized directly on the acquired images. Results The average mineral density of artificial WSL was 1.57 ± 0.21 g/cm3, compared to sound enamel with a mean mineral density of 2.9 ± 0.06 g/cm3. The mean lesion depth of 167.76 ± 0.03 µm for artificial WSL varied slightly between individual samples. The artificial WSL did have a highly mineralized surface overlying the body of the lesion, which is characteristic of subsurface lesions; however, the lesion itself was shallower when compared to naturally occurring WSL. The OCT also detected WSL and provided an estimate of lesion depth and distance from Conclusion In summary, we have developed an experimental in vitro protocol to create artificial WSL that mimics natural caries lesions. OCT produced live scans, which allowed the detection of WSL, whereas the microCT measurements provided precise information on lesion depth and mineral density.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".