Estimating adult cerebral oxygen saturation from time-resolved near-infrared spectroscopy measurements with few discrete wavelengths at a single source-detector distance
Bibliographic record
Abstract
Time-resolved Near-Infrared Spectroscopy (trNIRS) methods typically use multiple wavelengths and source-detector distances in conjunction with a solution of the diffusion approximation to quantify tissue blood content and oxygenation. This approach can be both computationally intensive and costly, as multiple detectors are required. We propose a novel two-layer fitting approach for multi-wavelength trNIRS, which uses a single detector while providing accurate estimates of cerebral oxygen saturation (ScO2) and hemoglobin content. The method uses a multi-step fitting algorithm to establish rough estimates of the absorption and scattering coefficients in the extracerebral layer and the brain, and subsequently refine those estimates, to improve accuracy while reducing crosstalk and complexity. Validation was conducted using Monte Carlo simulations in a realistic adult head model with appropriate optical properties at 680nm, 750nm, 800nm, and 830nm. The detector was located 30 mm anteriorly from the source, which was placed 50 mm above the right temple. Scalp oxygen saturation (SO2) (50%, 60%, and 70%) and ScO2 (40%-80%, 2% increments) were varied independently. The recovered ScO2 had a difference (mean±standard deviation) of 2.31±2.93% from inputted values, and cerebral total hemoglobin was recovered with a difference of 2.94±3.47%. Such high accuracy demonstrates the robustness of this computationally efficient two-layer fitting approach for analyzing multi-wavelength trNIRS measurements acquired with a single detector. Future work will involve validating the technique in tissue mimicking phantoms and animal studies.
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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 | 0.001 |
| 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 | 0.001 | 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".