Low Cost Adaptive Optics System for Retinal Imaging
Bibliographic record
Abstract
This paper presents the design of an Adaptive Optics (AO) system for retinal imaging applications. The development of retinal imaging systems allows for early diagnosis of eye diseases. Such systems can increase the quality of life of patients as well as curtail increasing health care costs through early eye disease detection and treatment. Until recently, AO systems have been prohibitively expensive and cumbersome. This has been mainly due to the size and cost of flexible membrane mirrors normally used as the aberration correction device. Recent developments in the technology of Microelectromechanical System (MEMS) based actuators allow the implementation of AO systems which would have been difficult to implement a few years ago due to exorbitant costs. The aim of this paper is to present the design of a compact and flexible low cost AO system using off the shelf components to measure and compensate for the aberrations of the eye. The design is based around the system’s main components which include a 52 channel magnetically actuated deformable membrane mirror, a Shack Hartmann wavefront sensor and a control system which runs on a single processor personal computer. All the components are commercially available. The use of the MEMS-based magnetically actuated mirror allows for increased resolution and force compared to conventional membrane mirrors designed mainly for use in astronomical applications. The performance of the closed-loop system is evaluated through experiments. Although designed as a diagnostic tool for eye diseases, such a system will find a number of applications in basic research in the visual sciences, including the study of microscopic structures in the living retina that could not be seen before. Optometrists, retinal surgeons, and ophthalmologists will also benefit from using such a system, through potential improvements on commonly used instruments such as phoropters and fundus cameras.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".