Near-infrared autofluorescence imaging of cutaneous melanins and human skin in vivo
Bibliographic record
Abstract
In recent years, near-infrared (NIR) autofluorescence imaging has been explored as a novel technique for tissue evaluation and diagnosis. We present an NIR fluorescence imaging system optimized for the dermatologic clinical setting, with particular utility for the direct characterization of cutaneous melanins in vivo. A 785-nm diode laser is coupled into a ring light guide to uniformly illuminate the skin. A bandpass filter is used to purify the laser light for fluorescence excitation, while a long-pass filter is used to block the main laser wavelength but pass the spontaneous components for NIR reflectance imaging. A computer-controlled filter holder is used to switch these two filters to select between reflectance and fluorescence imaging modes. Both the reflectance and fluorescence photons are collected by an NIR-sensitive charge-coupled device (CCD) camera to form the respective images. Preliminary results show that cutaneous melanin in pigmented skin disorders emits higher NIR autofluorescence than surrounding normal tissue. This confirmed our previous findings from NIR fluorescence spectroscopy study of cutaneous melanins and provides a new approach to directly image the distributions of cutaneous melanins in the skin. In-vivo NIR autofluorescence images may be useful for clinical evaluation and diagnosis of pigmented skin lesions, including melanoma.
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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) | 0.002 | 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".