Simulated optical performance of soft contact lenses on the eye
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of soft contact lens eye-fit on optical power by computational modelling and to produce correction maps for reversing this impact during the design process. METHODS: Finite element models of spherical and toric hydrogel contact lenses at varying nominal powers of -20 D to +20 D, base curves radii (R1b) of 8.2, 8.5, 8.8 mm, and overall diameters (d3) of 14.5, 15.0, 15.5 mm were generated. Lenses were fitted to computational eye models generated with human eyes' topography data. Combined eye-lens simulations were run under the boundary conditions of the tears' surface tension between the contact lens and the eye in addition to the eyelid blink pressure. Lens optical zone power changes were calculated through computational light-ray tracing methods following each simulation. RESULTS: Effective power changes (EPC) were affected negatively for all toric simulated lenses with power varying from -20 D to +20 D. Spherical lenses demonstrated similar behaviour, however with some positive EPC over the power range from -20 D to -10 D for spherical power (SPH) lenses. EPC assessment was between +0.25 D and -0.5 D for most lenses, however, lenses with prescriptions from +10 D to +20 D incurred EPC outside this range. The spherical lenses showed a maximum effective power change of +1.046 ± 0.338 D (Average Eye), and a minimum of -3.278 ± 0.731 D (Steep Eye). Similarly, the toric lenses showed a maximum of +1.501 ± 0.338 D (Average Eye), and a minimum of -3.514 ± 0.731 D (Steep Eye). EPC trends, along with minimum and maximum power, generally increased negatively as nominal lens prescription increased positively. Contact lens base curve selection affected the assessed effective power change for both spherical and toric lenses. The effect from lens total diameter for spherical lenses was less substantial than that for toric lenses. CONCLUSIONS: This study considered the impact of soft contact lens design parameters on effective optical power changes (EPC) after eye-fit. Spherical lenses experienced more EPC of clinical significance (>0.25 D) than toric lenses. Both types of lenses, spherical and toric (simple astigmatism), demonstrated similar trends in EPC on fitting from -20 D to +20 D, with lenses in the extremely positive and the extremely negative prescriptions demonstrating the highest EPCs. The lens base curve impacted the extent of EPC observed, with flatter base curves experiencing less power change. Diameter proved to impact toric lenses more than spherical ones, however generally the diameter has less effect on power change than base curve selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".