Predictive Modeling for Ophthalmic Lens Design Optimization
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Solution Overview
Problem
Existing methods for modeling visual acuity of ophthalmic lenses are not highly accurate when compared with clinical data, and they offer limited predictability for vision factors such as contrast sensitivity, failing to provide effective feedback for optimizing lens design based on clinical implementations.
Innovation Solution
A system and method for predictive modeling that applies mathematical modeling to physiological eye models, such as the 46 Piers eye models, to simulate visual acuity and contrast sensitivity, and uses clinical data feedback to evaluate and optimize ophthalmic lens design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MTF and NTF intersection methods are used to model visual acuity, then the modeling process is simple, but the accuracy compared with clinical data is not high
Solution Approach 1:
The patent implements a feedback mechanism where clinical data from actual lens implementations is fed back into the predictive modeling system. This feedback loop allows the system to continuously refine and optimize lens designs based on real-world performance data, thereby improving visual acuity modeling accuracy while managing system complexity through iterative improvement.
Solution Approach 2:
The system performs preliminary predictive modeling and optimization before actual lens manufacturing and clinical implementation. By simulating visual acuity and optimizing lens designs in advance using the predictive model, the system identifies potential issues and optimizes parameters before real-world deployment, improving accuracy while reducing the need for complex post-manufacturing adjustments.
2Measurement precision
If wavefront aberration techniques are used for preclinical testing, then alternative testing methods are provided, but the results have not comported well with clinical data
Solution Approach 1:
The patent establishes a feedback mechanism where clinical outcomes from lens implementations are systematically collected and fed back into the predictive modeling system. This allows continuous refinement of the modeling algorithms to better correlate with clinical data, improving measurement precision while maintaining ease of manufacture through automated optimization processes.
Solution Approach 2:
The system optimizes multiple lens parameters simultaneously including surface geometry, material properties, and optical characteristics. By changing and optimizing these parameters based on predictive modeling results, the system achieves better clinical data correlation while maintaining ease of manufacture through systematic parameter optimization rather than trial-and-error approaches.
3Adaptability or versatility
If MTF and wavefront aberration modeling tests are applied to specific vision conditions, then targeted analysis is achieved, but the predictability of VA and contrast sensitivity is limited
Solution Approach 1:
The patent creates a universal predictive modeling system that can handle multiple vision conditions and lens types through a single integrated platform. The system incorporates diverse eye models representing different patient populations and can evaluate various lens designs (intraocular lenses, contact lenses, spectacles) under different viewing conditions, thereby improving both adaptability and visual acuity predictability simultaneously.
Solution Approach 2:
The system uses composite eye models that integrate multiple physiological parameters and characteristics to represent real patient eyes more accurately. By combining various models of the eye's optical system, neural processing, and individual variations, the system achieves better predictability of visual acuity and contrast sensitivity across different vision conditions while maintaining versatility.
4Measurement precision
If clinical data feedback is implemented to evaluate and optimize lens design, then modeling accuracy improves, but the system complexity increases
Solution Approach 1:
The patent implements a structured feedback system where clinical data is systematically collected, processed, and integrated into the predictive modeling framework. This feedback mechanism improves modeling accuracy by continuously refining the models based on real-world performance, while the systematic approach manages system complexity through organized data flow and automated processing procedures.
Solution Approach 2:
The system uses simplified digital representations and models of the eye and lens systems that capture essential characteristics without requiring complete complexity. By creating accurate but computationally manageable copies of the optical systems, the patent achieves high modeling accuracy while controlling system complexity through efficient digital modeling rather than exhaustive physical simulation.
Data Source
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AI summary
An apparatus, system and method for predictive modeling to design, evaluate and optimize ophthalmic lenses is disclosed. Ophthalmic lenses may include, for example, contacts, glasses or intraocular lenses (lOLs). The apparatus, system and method may include a design tool for designing a lens for implantation in an eye having a plurality of characteristics, a simulator for simulating performance of the lens in at least one modeled eye having the plurality of characteristics, at least one input for receiving clinical performance of the lens in the eye having the plurality of characteristics, a comparator for comparing outcomes of the clinical performance and the simulated performance, and an optimizer for optimizing a subsequent one of the outcome of the clinical performance responsive to modification of the lens in accordance with modification to the simulated performance.