Targeted spectroscopy in the eye fundus
Bibliographic record
Abstract
SignificanceThe assessment of biomarkers in the eye is rapidly gaining traction for the screening, diagnosis, and monitoring of ocular and neurological diseases. Targeted ocular spectroscopy is a technology that enables concurrent imaging of the eye fundus and analysis of high-quality spectra from a targeted region within the imaged area. This provides structural, compositional, and functional information of specific regions of the eye fundus from a non-invasive approach to ocular biomarker detection.AimThe aim of our study was to demonstrate the multimodal functionality and validation of targeted ocular spectroscopy. This was done in vitro, using a reference target and a model eye, and in vivo.ApproachImages and spectra from different regions of a reference target and a model eye were acquired and analyzed to validate the system. Targeted ocular fluorescence spectroscopy was also demonstrated with the same model. Subsequently, in vivo imaging and diffuse reflectance spectra were acquired to assess blood oxygen saturation in the optic nerve head and the parafovea of healthy subjects.ResultsTests conducted with the reference target showed accurate spectral analysis within specific areas of the imaging space. In the model eye, distinct spectral signatures were observed for the optic disc, blood vessels, the retina, and the macula, consistent with the variations in tissue composition and functions between these regions. An ocular oximetry algorithm was applied to in vivo spectra from the optic nerve head and parafovea of healthy patients, showing significant differences in blood oxygen saturation. Finally, targeted fluorescence spectral analysis was performed in vitro.ConclusionsDiffuse reflectance and fluorescence spectroscopy in specific regions of the eye fundus open the door to a whole new range of monitoring and diagnostic capabilities, from assessment of oxygenation in glaucoma and diabetic retinopathy to photo-oxidation and photodegradation in age-related macular degeneration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".