Optic nerve oximetry mapping using a novel metabolic hyperspectral retinal camera
Bibliographic record
Abstract
Abstract Purpose Oximetry measurement of the principal retinal vessels represents a first step towards understanding retinal metabolic state. Spectral imaging is expected to significantly enhance metabolic state evaluation via local fundus oximetry determination. In this preliminary study, we focus on the oximetry of the entire optic nerve head (ONH) microvasculature from datasets obtained with a novel metabolic hyperspectral retinal camera (MHRC). Methods Five healthy volunteers had retinal images captured between 490‐650 nm in steps of 5 nm using a prototype MHRC (Optina Diagnostics, Montreal, Canada) based on a tunable laser source that permits the selection of a specific wavelength (2 nm bandwidth) from a supercontinuum source. Each subject's data was fit to a model where oxy‐ and deoxyhemoglobin are the main absorbers and scattering is modeled by a log(1/wavelength) term. The fitted parameters were used to extract an estimation of oxygen saturation and total hemoglobin content. Results The local oximetry and hemoglobin content maps over the entire ONH microvasculature were extracted for all subjects from the spectral‐rich information. Physiologically plausible oxygen saturation values were obtained for all subjects over this region (mean 58 +/‐ 6 %). Conclusion The oximetry and hemoglobin content maps of the ONH microvasculature obtained with the MHRC could ultimately contribute to the diagnostic and optimal management of diseases affecting the ONH such as glaucoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".