Visual Field Test Device Adaptive Luminance Thresholds
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Solution Overview
Problem
Current visual field tests are inefficient and time-consuming, and there is a need to obtain accurate test values while shortening the test duration.
Innovation Solution
A visual field test device and method that utilize big data to determine test conditions and convergence end points by adjusting luminance thresholds based on probability density functions, iteratively refining the stimulation threshold and probability density functions until a predetermined standard deviation is reached, thereby optimizing test duration and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a predetermined fixed luminance difference is used between visual targets in sequential visual field tests, then the test procedure is simple and easy to implement, but the test time cannot be sufficiently shortened and measurement precision is compromised
Solution Approach 1:
The patent applies dynamics by making the luminance threshold adaptive rather than fixed. The system dynamically adjusts the luminance difference between visual targets based on real-time test results and accumulated big data, allowing the test procedure to evolve and optimize itself during and across multiple tests, thereby reducing test time while maintaining precision.
Solution Approach 2:
The patent changes the parameter of luminance difference from a fixed predetermined value to a dynamically adjusted value based on probability density functions derived from big data. This parameter change enables the system to optimize test efficiency and accuracy by adapting the luminance threshold to individual patient characteristics and test progress.
2Productivity
If traditional visual field test methods are used with fixed luminance thresholds, then the test procedure is straightforward, but the test duration is lengthy and efficiency is low
Solution Approach 1:
The system performs self-service by automatically analyzing accumulated big data to generate probability density functions and autonomously determining optimal luminance thresholds for each test. The device self-optimizes the test procedure without requiring manual intervention or complex external control, thereby improving efficiency while managing complexity internally.
Solution Approach 2:
The patent implements feedback mechanisms where test results are continuously fed back into the big data repository, which then updates the probability density functions. This feedback loop allows the system to learn from each test and progressively improve future test efficiency, balancing productivity gains with manageable complexity through automated adaptation.
3Measurement precision
If big data and probability density functions are used to dynamically determine luminance thresholds, then test time is significantly shortened and accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and accumulating big data from multiple sources before actual testing. Probability density functions are pre-calculated based on historical data, allowing the system to quickly reference and apply optimized thresholds during testing without performing complex real-time calculations, thus improving precision while managing computational complexity.
Data Source
AI summary
A visual field test device includes: first probability density function acquisition unit performs step (1) of obtaining a probability density function f(x1) for a result value x1 obtained in a first visual field test; a stimulation threshold determination unit performs step (2) of setting a stimulation threshold t1 to a value in the range of x1 in the f(x1); a test result acquisition unit performs step (3) of obtaining test results indicating if a result greater than or equal to t1 was obtained in the first visual field test; a second probability density function acquisition unit performs step (4) of obtaining a probability density function f(x2) by removing probability density values that's greater than or equal to t1, or removing values that are less than t1 from f(x1); and a determination unit performs step (5) of determining if a standard deviation σ of the f(x2) is below a predetermined value.