Random Eye Generator for Refractive Surgery Simulation
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Solution Overview
Problem
Current eye models for simulating vision characteristics are limited in their ability to effectively evaluate refractive surgery treatments, as they often require extensive testing with a large number of eyes to achieve statistical significance, and may struggle with noise levels and variability in patient data.
Innovation Solution
The development of random eye generators that simulate human eyes with statistical parameters matching those of specific populations, allowing for the generation of numerous eyes to test refractive surgery techniques, including the use of Rayleigh and normal distributions for optical parameters, and integration with validated mechanisms like target controllers and virtual ablation systems for verification and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random eye generators are used to generate a large number of eyes for statistical significance, then the evaluation accuracy of treatment protocols is improved, but the computational complexity and time required for simulations increase
Solution Approach 1:
The patent pre-generates and stores population statistics data (refractive errors, corneal curvatures, axial lengths, etc.) from real patient populations before simulations begin. This preliminary preparation allows the random eye generator to quickly create statistically accurate virtual eyes without performing time-consuming real-time calculations during treatment protocol evaluations
Solution Approach 2:
The patent creates virtual copies of real patient eyes by generating synthetic eye models that replicate the statistical characteristics of actual populations. These copied virtual eyes preserve the essential statistical properties (distributions of optical parameters) without requiring access to or processing of actual patient data during simulations
2Reliability
If validated mechanisms like target controllers and virtual ablation systems are integrated for verification and validation, then the reliability of treatment protocol evaluation is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a random eye generator as an intermediary component that bridges between treatment protocol definitions and validation mechanisms. This intermediary layer manages the complexity by handling eye model generation and statistical parameter management separately, allowing the target controller and virtual ablation system to focus on their specific validation functions without dealing with the complexity of population statistics
Solution Approach 2:
The patent divides the validation system into distinct modular components: (1) random eye generator for creating virtual patient populations, (2) target controller for defining treatment parameters, (3) virtual ablation system for simulating treatment delivery, and (4) evaluation module for analyzing results. Each module handles specific tasks independently, reducing overall system complexity while improving validation reliability
Data Source
AI summary
Random human eye generators are provided for use in evaluating aspects of treatment in refractive surgery or other therapeutic vision modalities. Exemplary random eye generators include an optical parameter such as a manifest refractive sphere parameter or a wavefront sphere parameter, and incorporate a Rayleigh distribution for such parameters.