Visualization of cutaneous hemoglobin oxygenation and skin hydration using near‐infrared spectroscopic imaging
Bibliographic record
Abstract
BACKGROUND/AIMS: The visualization of skin hemodynamics and tissue water content has important implications in a number of areas of dermatology, plastic surgery, and clinical skin evaluation. The aim of this study was to develop instrumentation and techniques for infrared spectroscopic imaging, and to evaluate whether they can be used to make objective assessments of skin health, perhaps even before clinical signs are evident. METHODS: A liquid-crystal tunable filter was mounted on the front of the objective lens of an infrared-sensitive charge-coupled device digital camera. Sets of narrow-band images of skin were acquired in vivo at wavelength intervals of 10 nm from 650 to 1050 nm, under computer control. The data processing techniques used to extract interpretable clinical information from the raw image sets included normalization, ratios, and multivariate analysis. RESULTS: To highlight the capabilities of these techniques, results are presented of two studies that generated spectroscopic images. One examined a volunteer's forearm subjected to short interruptions of blood flow, and the other followed changes in a skin flap elevated on a rat model. The data sets were processed in different ways to determine several skin and blood parameters, in particular hemoglobin oxygen saturation, blood volume, and skin hydration. Variations in these parameters were followed non-invasively as a function of time and location to study the skin's response to blood flow changes, and to predict the viability of the skin. CONCLUSION: Near-infrared reflectance spectroscopic imaging is demonstrated to be a powerful augmentation to the standard clinical assessment of skin.
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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".