Estimating retinal blood oxygenation from diffuse reflectance spectra of semi-infinite tissue using principal component analysis
Bibliographic record
Abstract
Diffuse reflectance spectroscopy (DRS) is a promising technique for non-invasive monitoring of tissue oxygen saturation (StO2). However, the interpretation of DRS data can be complicated by the presence of confounding factors such as the volume fraction of blood, tissue scattering, and lipid content which both absorb and scatter. Principal component analysis (PCA) is a multivariate statistical method that can help overcome these challenges by extracting relevant information from complex datasets and providing new dimensions used to estimate parameters such as concentrations. In this study, we present a PCA-based algorithm for estimating retinal StO2 from DRS measurements. We evaluated the performance of our algorithm using simulated data and experimental measurements on a retinal tissue phantom model. Our results show that the PCA-based algorithm can estimate the value of StO2 with a root-mean-square error of 6.38% in the presence of confounding factors. Our study demonstrates the potential of PCA as a powerful tool for extracting the concentration of components from complex DRS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".