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Simulating the Mechanics of Lens Accommodation via a Manual Lens Stretcher

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Mathematical model for evaluating soft contact lens fit.

¹*MPhil, PhD, FCOptom, FAAO Visioncare Research Ltd, Farnham, United Kingdom.

Optometry and Vision Science : Official Publication of the American Academy of Optometry

|June 6, 2014

Summary

This summary is machine-generated.

A novel computer model helps understand soft contact lens (SCL) fit by analyzing corneal and lens parameters. Ocular and lens characteristics significantly influence SCL fit, impacting comfort and vision.

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Area of Science:

Ophthalmic optics
Biomechanical modeling
Corneal topography

Background:

Soft contact lenses (SCLs) are widely used by over 100 million people globally.
Factors influencing SCL fit are not fully understood, despite widespread use.

Purpose of the Study:

To evaluate how varying lens and ocular topography parameters affect soft contact lens (SCL) fit.
To utilize a novel computer spreadsheet model for this evaluation.

Main Methods:

Developed a spreadsheet-based computer model with an ellipto-conical corneal model.
Incorporated population data on corneoscleral topography.
Systematically varied parameters: corneal curvature, diameter, shape factor, corneoscleral junction angle, lens base curve (BC), and diameter.
Calculated lens edge strain as a predictor of lens tightness.

Main Results:

The ellipto-conical corneal model demonstrated superior accuracy compared to a simple elliptical model in measuring corneal sagittal height.
For average ocular parameters, a typical SCL showed 2.7% edge strain.
Extreme ocular parameters (small, flat, aspheric cornea) resulted in the tightest fit (8.5% strain), while others (large, steep, less aspheric) led to the loosest fit (-2.6%).
Minor changes in BC or diameter had limited impact on edge strain (<2.5% and <2% respectively).
Variations in corneoscleral junction angle did not critically affect lens fit in typical scenarios.

Conclusions:

A novel ellipto-conical corneal model combined with spreadsheet modeling effectively aids in understanding SCL fit determinants.
This approach provides insights into how ocular and lens parameters influence the tightness or looseness of SCLs.