Targeted spectroscopy in the eye fundus
Bibliographic record
Abstract
Abstract Significance The 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 new technology that enables the user to concurrently image the eye fundus and acquire high quality spectra from a targeted region –1.5 degrees– within the imaged area. The combination of imaging and high-sensitivity spectroscopy provides structural, compositional, and functional information of selected regions of the eye fundus. This opens the door to new, non-invasive approaches to the detection of biomarkers in the eye. Aim The aim of this study was to demonstrate the multi-modal functionality and validation of the targeted ocular spectroscopy developed. This was done in vitro, using a reference target and a model eye, and in vivo. Approach Images and spectra from different regions of a reference target and a model eye were acquired and analyzed to validate the system. The same eye model was used to obtain fluorescence images and spectra, highlighting the capability of the system to also perform targeted ocular fluorescence spectroscopy. 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. Results Tests conducted with the reference target showed that spectral analysis could be accurately performed within specific areas of the imaging space. Moving to the model eye, distinct spectral signatures were observed for the targeted spectral analysis done in the optic disc, the retina and the macula, consistent with the variations in tissue composition and functions between these regions mimicked by the model eye. Further, it was shown that the targeted spectral analysis could also be performed in a fluorescence mode to distinguish various fluorophores present within the imaging space. Finally, in vivo ocular oximetry experiments performed in the optic nerve head and parafovea of healthy patients showed significant differences in blood oxygen saturation between these regions (p = 0.004). Conclusions Enabling non-invasive, sensitive diffuse reflectance and fluorescence spectroscopy in specific regions of the eye fundus opens 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 photo-degradation in age-related macular degeneration.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".