Indocyanine green–assisted dental imaging in the first and second near‐infrared windows as compared with X‐ray imaging
Bibliographic record
Abstract
Indocyanine green (ICG) has been widely used in medical imaging, such as in retinal angiography. Here, we describe a pilot ex vivo study of ICG-assisted near-infrared fluorescence (NIRF) dental imaging in the first (700-950 nm for ICG-NIRF-I) and second (1000-1700 nm for ICG-NIRF-II) NIR windows using human extracted teeth; our study is compared with the traditional prevalent X-ray imaging and NIR II illumination (NIRi-II, 1310 nm) without ICG enhancement. The results show that ICG fluorescence has much better imaging contrast in both windows compared with NIRi-II (by quantitatively comparing NIR intensity of the critical neighboring structures, such as enamel and dentin). Cracked teeth, notoriously hard to diagnose by dental X-ray and computed tomography, were clearly profiled in NIRF dental imaging. An insidious occlusal caries, missing in X-ray imaging, became a bright dot that was readily observed in ICG-NIRF-I images. For dental decay, NIRF imaging with ICG enhancement could clearly delineate the decay boundary. NIRF in both windows distinguished interproximal and occlusal superficial caries. Overall, ICG-assisted NIRF dental imaging has unique advantages in identifying cracked teeth and insidious caries. The two NIR imaging windows used in our study might one day serve as noninvasive and nonionizing-radiation methods for the diagnosis of critical dental diseases in situ.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".