Detection and analysis of enamel cracks by ICG‐NIR fluorescence dental imaging
Bibliographic record
Abstract
Cracked teeth are the third most common cause of tooth loss, but there is no reliable imaging tool for the diagnosis of cracks. Here, we demonstrate the feasibility of indocyanine green near-infrared fluorescence (ICG-NIRF) dental imaging for the detection of enamel cracks and enamel-dentin cracks in vitro in the first (ICG-NIRF-I, 700-950 nm) and second (ICG-NIRF-II, 950-1700 nm) imaging windows with transmission excitation light, and compared ICG-NIRF with conventional NIR illumination-II (NIRi-II) and X-ray imaging. Dentin cracks were detected by CT scan, while most enamel cracks, undetectable under X-ray imaging, were clearly visible in NIR images. We found that ICG-NIRF-II detected cracks more effectively than NIRi-II, and that light orientation is an important factor for crack detection: an angled exposure obtained better image contrast of cracks than parallel exposure, as it created a shadow under the crack. Crack depth could be evaluated from the crack shadow in ICG-NIRF and NIRi-II images; from this shadow we could determine crack depth and discriminate enamel-dentin cracks from craze lines. Cracks could be observed clearly from ICG-NIRF images with 1-min ICG tooth immersion, although longer ICG immersion produced images with greater contrast. Overall, our data show that ICG-NIRF dental imaging is a useful tool for diagnosing cracked teeth at an early stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".