Eye Disease Risk Score Determination Using Physiological Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting eye diseases like age-related macular degeneration (AMD) often fail to detect early signs, leading to late-stage diagnosis and irreversible visual loss, as they rely on visual acuity tests that do not provide information on retinal function changes in early AMD.
Innovation Solution
A computer-based method that determines a risk score for eye diseases by using multiple eye-mediated physiological parameters such as dark adaptation, pupil light reflex, flicker sensitivity, and other tests, along with life profile and environment data, to detect early functional changes and provide an updated risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual acuity testing is used to diagnose eye diseases, then the testing process is simple and quick, but early eye disease cannot be detected as visual acuity changes in early AMD are undetectable
Solution Approach 1:
The patent segments the assessment into multiple independent physiological parameter measurements (pupil light reflex, dark adaptation, flicker sensitivity, visual acuity) rather than relying on a single test. Each parameter provides specific information about different aspects of retinal function, allowing early detection while maintaining operational simplicity through standardized testing protocols.
Solution Approach 2:
The patent transitions from one-dimensional visual acuity measurement to multi-dimensional physiological parameter assessment. By measuring multiple independent parameters (pupil response characteristics, dark adaptation curves, flicker sensitivity thresholds), the system detects early retinal dysfunction that would be invisible in single-parameter testing, effectively adding measurement dimensions to capture subtle pathological changes.
2Measurement precision
If multiple eye-mediated physiological parameters are used to determine risk score, then early detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent employs a single multi-functional testing system that measures multiple physiological parameters (pupil light reflex, dark adaptation, flicker sensitivity, visual acuity) through integrated optical and electronic components. This universal device performs diverse measurements without requiring separate specialized equipment for each parameter, thereby improving early detection accuracy while controlling device complexity through functional integration.
Solution Approach 2:
The system measures multiple physiological parameters that change in response to early retinal dysfunction. By monitoring changes in pupil response characteristics, dark adaptation rates, flicker sensitivity thresholds, and visual acuity, the system detects subtle pathological changes before they manifest as significant visual loss, achieving high detection accuracy through sensitive parameter monitoring rather than complex device architecture.
3Measurement precision
If comprehensive physiological parameters and life profile data are collected, then risk score accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where multiple physiological parameter measurements and life profile data are systematically collected, processed, and integrated to generate an updated risk score. This feedback loop allows the system to continuously refine the risk assessment by comparing current measurements with established thresholds and previous results, achieving high accuracy through iterative data integration rather than complex one-time processing.
Solution Approach 2:
The system performs preliminary processing of physiological parameters and life profile data by establishing reference ranges, detection thresholds, and risk scoring criteria before actual patient assessment. This preliminary preparation of data processing frameworks and algorithms enables accurate risk score calculation during patient testing without requiring complex real-time processing, as the analytical framework is pre-established based on population studies and clinical guidelines.
Data Source
Figure 1
AI summary
The invention relates to a method, implemented by computer means, for determining a risk score of an eye disease for a user, the method comprising: - a first eye-mediated physiological parameter providing step (S10), during which a first eye-mediated physiological parameter indicative of a first eye-mediated perception or behaviour of the user is provided, - a second eye-mediated physiological parameter providing step (S20), during which a second eye-mediated physiological parameter indicative of a second eye-mediated perception or behaviour of the user is provided, and - a risk score determining step (S30), during which the risk score of eye disease is determined based on the first eye-mediated physiological parameter and on the second eye-mediated physiological parameter.