Minimum spectral resolution for continuous-wave hyperspectral near-infrared tissue spectroscopy
Bibliographic record
Abstract
Significance: Continuous-wave hyperspectral near-infrared spectroscopy (h-NIRS) is a reliable and cost-effective technique for noninvasive monitoring of tissue blood content and oxygenation at the point-of-care; however, most h-NIRS devices are based on expensive custom-built spectrometers. For widespread adoption, low-cost, miniature, off-the-shelf spectrometers are needed. To guide the development of such spectrometers, a standard for spectral resolution must first be defined. Aim: We aim to identify the minimum spectral resolution needed for h-NIRS devices to accurately measure oxy- and deoxy-hemoglobin (HbO and Hb) concentrations in tissue. Approach: h-NIRS measurements were acquired from fully oxygenated and deoxygenated blood-lipid phantoms at 13 spectral resolutions. Data for other oxygenation levels were simulated using NIRFAST. HbO and Hb concentrations were estimated at each resolution and compared with the ground truth hemoglobin concentration. Results: The concentration of Hb was estimated with high accuracy for resolutions up to 10 nm, whereas HbO estimates were more variable. For both chromophores, the accuracy of the estimation gradually decreased with resolutions beyond 10 nm. Conclusions: Spectral resolutions up to 10 nm can be used for h-NIRS without compromising the accuracy of estimating tissue blood content and oxygenation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".