Dark Adaptometer Rod Recovery Rate Extraction
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Solution Overview
Problem
Current methods for measuring dark adaptation in the retina are lengthy and challenging to interpret due to their non-linear nature, making it difficult to determine key recovery rates, especially in the context of age-related macular degeneration, where the rate of rod recovery is critical.
Innovation Solution
A measurement apparatus and method that utilize a combination of bleaching light, background light, and target light to reduce testing duration by fitting smooth, continuous models to threshold data, allowing for real-time analysis and determination of rod recovery rates within 10 minutes, using models that account for cone and rod recovery phases with transition functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dark adaptation measurement methods are used, then comprehensive retinal recovery data is obtained, but testing duration becomes excessively long (30 minutes or more)
Solution Approach 1:
The patent extracts and isolates the critical measurement parameter (rod recovery rate S2) from the complete dark adaptation curve analysis. By using a simplified exponential decay model that focuses specifically on the rod-dominated phase rather than attempting to characterize all three recovery components, the method obtains the essential diagnostic information in a much shorter time frame while maintaining measurement precision for the clinically relevant parameter.
Solution Approach 2:
The patent changes the measurement approach by shifting from comprehensive curve characterization to targeted parameter extraction. The method uses a fitted exponential decay model to directly estimate the rod recovery rate S2 parameter, transforming the problem from analyzing the entire non-linear dark adaptation curve to measuring a specific kinetic parameter, thereby reducing testing duration while preserving diagnostic accuracy.
2Loss of information
If complete dark adaptation curve analysis is performed, then comprehensive retinal function assessment is achieved, but data interpretation becomes mathematically complex
Solution Approach 1:
The patent extracts the essential diagnostic information (rod recovery rate S2) from the complex dark adaptation curve by focusing on the rod-dominated phase. The method uses a simplified exponential decay model that bypasses the mathematical complexity of characterizing all three recovery components (cone-dominated exponential phase, rod-dominated linear phase, and terminal phase), thereby reducing analytical complexity while retaining the critical diagnostic parameter for AMD assessment.
Solution Approach 2:
The patent replaces the complex mathematical analysis of non-linear dark adaptation curves with a simpler exponential decay fitting approach. By substituting the traditional multi-phase curve analysis methodology with a focused kinetic parameter estimation using exponential fitting, the invention simplifies the data interpretation process while maintaining the ability to assess retinal function, particularly rod recovery in AMD patients.
3Measurement precision
If traditional threshold measurement methods are used, then detailed recovery phase characterization is achieved, but determination of rod recovery rate becomes unreliable
Solution Approach 1:
The patent extracts the rod recovery rate parameter S2 directly from the threshold data using exponential decay fitting, bypassing the need to visually or mathematically identify the transition points between recovery phases. By focusing on fitting the exponential model to the rod-dominated portion of the curve and extracting the kinetic parameter, the method achieves more reliable and objective measurement of rod recovery rate compared to traditional methods that require manual transition point identification.
Solution Approach 2:
The patent employs iterative exponential decay fitting that uses feedback from the measured threshold data to refine the estimation of the rod recovery rate S2 parameter. The fitting process continuously adjusts the model parameters to minimize the difference between predicted and actual threshold measurements, providing an objective and reliable determination of rod recovery kinetics without requiring manual identification of transition points in the dark adaptation curve.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables a significant reduction in testing time from 30 minutes to 10 minutes while providing accurate and confident estimates of rod recovery rates, such as S2, with computation times of 0.0129 seconds, compared to existing methods like the 'bootstrap' method, allowing for earlier termination of tests with sufficient confidence.
Implementation Method 1
The term 'bleach' is often used, because it induces a change in a photopigment contained in the retina (rhodopsin) to its colourless form
Implementation Method 2
The parameters of the model are indicated in Figure 3 which shows an exponential decay 210, due to cones, followed by a linear phase 220. The most important of these is the rate of rod recovery called S2 (lu/min), as represented by the linear phase, which represents the ability of the retina to restore photo-pigments to the rods from the outer retinal layers
Data Source
Figure 1~2
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Figure 5~6
AI summary
Embodiments of the present invention provide a computer-implemented method of determining a parameter indicative of dark adaptation of an eye, comprising receiving threshold data from a dark adaptometer indicative of a perception threshold of the eye, fitting first and second models to the threshold data, wherein the first model is associated with a first dark adaptation mechanism and the second model is associated with the first dark adaption mechanism and a second dark adaptation mechanism, determining a confidence associated with the fitting of each of the first and second models to the received threshold data, iteratively repeating the steps of receiving the threshold data and fitting the first and second models in dependence on the determined confidence,and outputting an indication of one or more parameters indicative of dark adaptation of an eye associated with one or both of the first and second models.