Multifocal Contact Lens Design Using Neural Contrast Sensitivity Weighting
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Solution Overview
Problem
Conventional multifocal lenses fail to adequately account for individual differences in neural contrast sensitivity between dominant and non-dominant eyes, leading to suboptimal visual performance, especially in correcting presbyopia and post-intraocular lens scenarios.
Innovation Solution
A method utilizing a predictive model that applies different weighting functions based on the neural contrast sensitivity function (NCSF) for each eye, incorporating a visual performance prediction model to optimize lens design, allowing for the calculation of optical transfer function and visual acuity without physical testing, and adjusting design parameters for improved distance and near vision correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multifocal lens designs are used, then general distance and near vision correction is provided, but individual differences in neural contrast sensitivity between dominant and non-dominant eyes are not accounted for, leading to suboptimal visual performance
Solution Approach 1:
The patent applies different weighting functions based on NCSF to the dominant and non-dominant eyes separately. The dominant eye receives a weighting function optimized for distance vision tasks, while the non-dominant eye receives a different weighting function optimized for its specific visual characteristics. This local differentiation allows each eye to be customized according to its individual neural contrast sensitivity profile, resolving the contradiction between general correction and individual customization.
2Manufacturing precision
If iterative optimization with physical testing is used, then lens design accuracy is improved, but design development time increases significantly
Solution Approach 1:
The patent employs a visual performance prediction model that creates a virtual copy of the optical testing process. Instead of physically manufacturing and testing each lens design iteration, the system uses computational models to predict visual performance outcomes. This virtual copying allows for rapid iterative optimization of lens designs based on NCSF weighting functions without the time-consuming physical prototyping and on-eye testing cycles, thus resolving the contradiction between design accuracy and development time.
Data Source
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AI summary
The invention provides methods for designing contact lenses that provides improved efficiency in lens design compared to conventional methods. It is a discovery of the invention that improved performance and reduced design time can be obtained by utilizing a visual performance prediction model as a part of the design process.