Customized aspheric intraocular lenses calculated with real ray tracing
Bibliographic record
Abstract
PURPOSE: To calculate the exact geometry of custom intraocular lenses (IOLs) for pseudophakic eyes and theoretically predict the residual wavefront error by real ray tracing based on Snell's law. SETTING: Centre for Ophthalmology, University Hospital, Tübingen, Germany. METHODS: Individual computer models were constructed based on measurements, including corneal topography and axial length. The geometry of custom spherical, aspheric, toric, and toric aspheric IOLs was calculated in an optimization process with real ray tracing to provide the minimum root mean square wavefront error. The geometric optical properties in terms of residual wavefront error was simulated and approximated by Zernike polynomials. RESULTS: Data from 45 pseudophakic eyes were used to construct the models. Defocus was almost completely corrected by the spherical IOL and astigmatism, by the toric IOL. The aspheric IOL strongly reduced spherical aberration but only slightly reduced total higher-order aberrations (HOAs); both theoretical predictions corresponded to clinical investigations of wavefront measurements in pseudophakic eyes with a spherical or aspheric IOL. CONCLUSIONS: Real ray tracing calculated the exact geometry of custom IOLs to provide the minimum wavefront error, going beyond simple diopter information. Results show spherical aberration can be significantly reduced with aspheric IOLs. However, the limited possible reduction of total HOAs, even perfectly positioned custom aspheric IOLs, may be a reason for the unclear results in studies assessing the potential benefit to visual performance of currently used aspheric IOLs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".