Adaptive optics scanning laser ophthalmoscopy and optical coherence tomography (AO-SLO-OCT) system for in vivo mouse retina imaging
Bibliographic record
Abstract
retinal diagnostics and are now commonly used in ophthalmology clinics as well as in vision science research. Adaptive optics (AO) technology enables high-fidelity correction of ocular aberrations, resulting in improved resolution and sensitivity for both SLO and OCT systems. The potential of gathering multi-modal cellular-resolution information in a single instrument is of great interest to the ophthalmic imaging community. Although similar instruments have been developed for imaging the human retina, developing such a system for mice will benefit basic science research and should help with further dissemination of AO technology. Here, we present our work integrating OCT into an existing mouse retinal AO-SLO system, resulting in a multi-modal AO-enhanced imaging system of the living mouse eye. The new system allows either independent or simultaneous data acquisition of AO-SLO and AO-OCT, depending on the requirements of specific scientific experiments. The system allows a data acquisition speed of 200 kHz A-scans/pixel rate for OCT and SLO, respectively. It offers ∼6 µm axial resolution for AO-OCT and a ∼1 µm lateral resolution for AO-SLO-OCT imaging.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".