Noise-Based Contrast Sensitivity Diagnostic Tool for Eye Disease Detection
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Solution Overview
Problem
Conventional contrast sensitivity (CS) tests, such as the Pelli-Robson CS chart, have limitations including poor sensitivity for detecting certain eye diseases, inability to differentiate among diseases, limited portability, and susceptibility to room lighting artifacts, making them inadequate for clinical use.
Innovation Solution
A method and diagnostic tool that assesses CS by presenting a series of scenes with targets on both uniform and luminance noise backgrounds, allowing for the evaluation of contrast sensitivity in the presence and absence of noise, using a computer system to display and monitor responses, and determining normal or abnormal CS to diagnose conditions like amblyopia, retinitis pigmentosa, and diabetic retinopathy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CS charts (e.g., Pelli-Robson) are used, then CS measurement is simple and portable, but sensitivity for detecting certain eye diseases is poor and ability to differentiate among diseases is limited
Solution Approach 1:
The test is segmented into two distinct parts: (1) CS measurement in the absence of noise using conventional charts, and (2) CS measurement in the presence of noise using displayed images with superimposed noise patterns. This segmentation allows each part to serve a specific diagnostic purpose while maintaining overall test manageability.
Solution Approach 2:
Visual noise is introduced as an intermediary element to enhance disease detection. The noise superimposed on test images acts as a mediator that reveals subtle visual dysfunctions not detectable by conventional means, thereby improving sensitivity for conditions like retinitis pigmentosa and diabetic retinopathy.
2Measurement precision
If conventional CS charts are used, then the test can be administered easily, but it cannot differentiate among possible causes of CS abnormality
Solution Approach 1:
The diagnostic process is divided into two segments: conventional CS testing and noise-based CS testing. By comparing results from both segments, clinicians can differentiate among disease causes. For example, retinitis pigmentosa patients show normal conventional CS but abnormal noise-based CS, while diabetic retinopathy patients show abnormalities in both.
Solution Approach 2:
The combined testing approach serves multiple diagnostic functions simultaneously: it measures overall CS, detects subtle dysfunctions, and differentiates among disease etiologies. This multi-functionality is achieved through the dual-component test structure that evaluates CS under different viewing conditions.
3Measurement precision
If computer-based CS measurements in white luminance noise are used, then test sensitivity is enhanced and disease detection is improved, but specialized hardware and software are required making it difficult to expand for general clinical use
Solution Approach 1:
Visual noise serves as an intermediary that can be superimposed on conventional chart images or displayed images using basic display capabilities. This approach maintains enhanced disease detection sensitivity while avoiding the need for specialized measurement hardware, as the noise can be generated and displayed using standard computer or mobile device screens.
4Length of moving object
If conventional CS charts are used, then portability is limited due to large size, but the test can be illuminated evenly
Solution Approach 1:
The test material is segmented into smaller, digitally displayable image elements rather than requiring a single large physical chart. These images can be displayed on portable devices with controlled illumination, eliminating the need for large physical charts while ensuring consistent viewing conditions through electronic display control.
Data Source
AI summary
Methods and diagnostic tools are provided for assessing contrast sensitivity in a subject in the presence and absence of luminance noise by: i) presenting to the subject a series of scenes, each scene comprising at least a first target having a preselected level of contrast superimposed on a uniform background and a second target having a preselected level of contrast superimposed on a luminance noise background, wherein in each successively presented scene the first and second targets that are superimposed on the uniform background and on the luminance noise background, respectively, have contrast levels that are different from the contrast levels of the first and second targets superimposed on the uniform background and on the luminance noise background, respectively, in the previously presented scene; ii) monitoring responses by the subject to step i); and iii) evaluating the contrast sensitivity of the subject based on the monitored responses.