Optimal imaging windows of indocyanine green‐assisted near‐infrared dental imaging with rat model and its comparison to X‐ray imaging
Bibliographic record
Abstract
In this study, we used rat animal model to compare the efficiency of indocyanine green (ICG)-assisted dental near-infrared fluorescence imaging with X-ray imaging, and we optimized the imaging window for both unerupted and erupted molars. The results show that the morphology of the dental structures was observed clearly from ICG-assisted dental images (especially through the endoscope). A better image contrast was easily acquired at the short imaging windows (<10 minutes) for unerupted and erupted molars. For unerupted molars, there is another optimized imaging window (48-96 hours) with a prominent glow-in-the-dark effect: only the molars remain bright. This study also revealed that the laser ablation of dental follicles can disrupt the molar development, and our method is able to efficiently detect laser-treated molars and acquire the precise morphology. Thus, ICG-assisted dental imaging has the potential to be a safer and more efficient imaging modality for the real-time diagnosis of dental diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".