Targeted retinal spectroscopy: Towards a localized assessment of biomarkers in the eye fundus
Bibliographic record
Abstract
Aims/Purpose: The non‐invasive evaluation of biomarkers for the screening, diagnosis, and monitoring of ocular and neurological diseases is receiving increasing attention. Targeted retinal spectroscopy (TRS) enables simultaneous imaging and high‐quality spectral analysis from specific regions of the eye fundus. It offers valuable information on the structure, composition, and function of retinal tissues. This study demonstrates the capabilities of TRS and assesses its effectiveness in retinal oximetry. Methods: The Zilia Ocular TRS platform was developed and evaluated. First, a reference target and a model eye were used to demonstrate the targeted spectral analysis. Then, Monte Carlo simulations were used to identify crucial TRS parameters—such as spectral range, resolution, noise, and tissue scattering—for retinal oximetry. Simultaneous imaging of the eye fundus and diffuse reflectance spectra acquisitions were performed in two targeted regions of the eye fundus of eight healthy subjects to determine blood oxygen saturation (StO2). Results: Experiments performed with the reference target and model eye showed precise and distinct spectral signatures for each targeted region. Simulation results showed that highest StO2 accuracy was obtained with a spectral range between 530 nm and 585 nm, while a resolution below 4 nm compromised the accuracy. In addition, acquisition areas larger than blood vessels led to an underestimation of StO2, and a linear correlation was found between the additive noise level and the variability of StO2. In vivo oximetry measurements revealed significant differences in StO2 between the optic nerve head and the parafovea. Conclusions: TRS opens up new possibilities for disease screening, diagnosis and monitoring, including assessing oximetry in glaucoma, diabetic retinopathy, age‐related macular degeneration, etc. To account for confounding factors, careful selection of acquisition parameters is crucial, as emphasized in this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".