Near Infrared Autofluorescence Lifetime Imaging of Human Retinal Pigment Epithelium Using Adaptive Optics Scanning Light Ophthalmoscopy
Bibliographic record
Abstract
Purpose: To demonstrate the first near-infrared adaptive optics fluorescence lifetime imaging ophthalmoscopy (NIR-AOFLIO) measurements in vivo of the human retinal pigment epithelial (RPE) cellular mosaic and to visualize lifetime changes at different retinal eccentricities. Methods: NIR reflectance and autofluorescence were captured using a custom adaptive optics scanning light ophthalmoscope in 10 healthy subjects (23-64 years old) at seven eccentricities and in two eyes with retinal abnormalities. Repeatability was assessed across two visits up to 8 weeks apart. Endogenous retinal fluorophores and hydrophobic whole retinal extracts of Abca4-/- pigmented and albino mice were imaged to probe the fluorescence origin of NIR-AOFLIO. Results: The RPE mosaic was resolved at all locations in five of seven younger subjects (<35 years old). The mean lifetime across near-peripheral regions (8° and 12°) was longer compared to near-foveal regions (0° and 2°). Repeatability across two visits showed moderate to excellent correlation (intraclass correlation: 0.88 [τm], 0.75 [τ1], 0.65 [τ2], 0.98 [a1]). The mean lifetime across drusen-containing eyes was longer than in age-matched healthy eyes. Fluorescence was observed in only the extracts from pigmented Abca4-/- mouse. Conclusions: NIR-AOFLIO was repeatable and allowed visualization of the RPE cellular mosaic. An observed signal in only the pigmented mouse extract infers the fluorescence signal originates predominantly from melanin. Variations observed across the retina with intermediate age-related macular degeneration suggest NIR-AOFLIO may act as a functional measure of a biomarker for in vivo monitoring of early alterations in retinal health.
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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 | 0.000 | 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".