The Placido Wavefront Sensor and Preliminary Measurement on a Mechanical Eye
Bibliographic record
Abstract
PURPOSE: The hardware and software of a novel wavefront sensor was developed (The sensor presented here is patent pending.). It has the same principal of the Hartmann-Shack (HS) and other sensors that are based on slope information for recovery of wavefront surface, but a different symmetry, and does not use individual microlenses. This polar symmetry might offer differences during practical measurements that may add value to current and well-established "gold standard" techniques. METHODS: The sensor consists of a set of concentric "half-donut" surfaces (longitudinally sectioned toroids) molded on an acrylic surface with a CCD located at the focal plane. When illuminated with a plane wavefront, it focuses a symmetric pattern of concentric discs on the CCD plane; for a distorted wavefront, a nonsymmetric disc pattern is formed (similar to images of a placido-based videokeratographer). From detection of shift in the radial direction, radial slopes are computed for a maximum of 2880 points, and the traditional least-squares procedure is used to fit these partial derivatives to a set of 15 conventional OSA-VSIA Zernike polynomials. Theoretical computations for several synthetic surfaces containing low-order aberration (LOA) and high-order aberration (HOA) were implemented for both the HS and the new sensor. RESULTS: Root mean square error (RMSE) in microns when theoretical data was taken as control, for HS sensor and new sensor, was 0.02 and 0.00003 for LOA (defocus, astigmatism) and 0.07 and 0.06 for HOA (coma, spherical, and higher terms), respectively. After this, practical preliminary measurements on a mechanical eye with a 5-mm pupil and 10 different defocus aberrations ranging from -5 D to 5 D, in steps of 1 D, were compared between sensors. RMSE for difference in measurements for HS and new sensor for sphere, cylinder, and axis, was 0.13 D, 0.07 D, and 11. Measurements were taken only on defocus aberrations. Qualitative images for astigmatism are shown. DISCUSSION: Although practical in vivo tests were not conducted in this first study, we also discuss certain possible alignment differences that may arise as a result of the different symmetry of the new sensor. To take any conclusive assumption regarding the accuracy and/or precision of this new sensor, when compared with other well-established sensors, statistically significant in vivo measurements will need to be conducted.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".