Multi-scale and -mode sensorless adaptive optics OCT for in vivo human retinal imaging (Conference Presentation)
Bibliographic record
Abstract
In this study we describe our novel Multi-Scale and multi-Mode Sensorless Adaptive Optics OCT system (MSM-SAO-OCT). Our system expands upon our previously reported work by introducing a zoomable collimator, phase calibration interferometer, and polarization diversity detection module. By using a zoomable collimator into the system setup, we allow an adjustable probing beam diameter without the need to change the optical setup, permitting imaging with both low and high lateral resolution (18 µm – 6 µm) at various Fields of View (FOV) within diffraction limited resolution. By employing SAO optimization algorithm, different morphological structures and microvasculature in a retina were clearly visualized after wavefront aberration correction with dual deformable optical elements – Variable Focus Lens (VFL) for defocus and a Multi-Actuator Adaptive Lens (MAL) for two astigmatisms. For retinal vasculature imaging, MSM-SAO-OCT system generates flow-specific contrast as measuring amplitude of complex variance from the multiple OCT B-scans from the same transverse location after stabilizing OCT signals in a phase using a static interference signal from phase calibration interferometer. In addition, the use of polarization diversity detection allows to create Degree Of Polarization Uniformity (DOPU) contrast using for visualization of the Retinal Pigment Epithelium (RPE) with its inherent tissue characteristic (polarization scrambling). In order to demonstrate functionality and clinical utility of the MSM-SAO-OCT system, in vivo human retinal imaging was performed on research subjects, and imaging results are presented and discussed.
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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) | 0.002 | 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".