Probabilistic Visual Field Modeling for Glaucoma Assessment
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Solution Overview
Problem
Current visual field testing methods, such as ophthalmic perimeters, do not effectively utilize spatial structure information from nearby locations, leading to incomplete and unreliable assessments of visual sensitivity, particularly in diagnosing diseases like glaucoma.
Innovation Solution
A probabilistic model is developed using connection strengths and noise measures across the visual field, allowing for the creation of a continuous probability distribution that adjusts testing configurations based on patient responses, providing more accurate and precise visual field mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ophthalmic perimeter testing is used to measure visual field locations, then sensitivity values can be obtained for isolated locations, but the spatial structure information from nearby locations is not utilized, leading to incomplete and unreliable assessments
Solution Approach 1:
The patent combines multiple isolated visual field measurements with spatial correlation modeling to create a unified probabilistic assessment. By merging individual location data with spatial relationship information from nearby locations, the system recovers the spatial structure that was lost in traditional isolated measurements, thereby improving reliability without requiring additional testing.
Solution Approach 2:
The patent introduces a probabilistic model as an intermediary between raw visual field measurements and final diagnostic conclusions. This model acts as a mediator that incorporates spatial structure information and connects discrete measurements to comprehensive visual field assessment, preventing information loss between the measurement device and the diagnostic interpretation.
2Reliability
If comprehensive visual field testing is performed to ensure accurate diagnosis, then reliable sensitivity data can be obtained, but testing time increases
Solution Approach 1:
The patent performs preliminary spatial correlation analysis and probabilistic modeling during the testing process itself, rather than requiring complete data collection first. By conducting preliminary assessments and adapting the testing protocol based on emerging patterns, the system achieves accurate diagnosis without requiring exhaustive testing of all visual field locations, thereby reducing testing time while maintaining diagnostic reliability.
Solution Approach 2:
The patent implements a dynamic testing protocol that adapts to patient responses in real-time. The testing configuration is adjusted based on probabilistic assessments of visual field sensitivity, allowing the system to focus measurements on critical areas and skip redundant locations, thus achieving accurate diagnosis more quickly than static comprehensive testing protocols.
3Ease of operation
If isolated visual field locations are tested individually, then measurement simplicity is maintained, but spatial relationships between locations are ignored, reducing measurement precision
Solution Approach 1:
The patent segments the visual field assessment into discrete measurable locations while simultaneously modeling the spatial relationships between them. This segmentation allows for simple individual measurements to be taken using traditional perimetry methods, while the probabilistic model reconstructs the spatial structure, thereby maintaining measurement simplicity while improving overall assessment precision.
Data Source
AI summary
In some example implementations, there is provided a method. The method may include determining a probabilistic model representing threshold sensitivities across the visual field, the probabilistic model determined based on the data, the connection strengths, and the noise values. Related systems, methods, and articles of manufacture are also disclosed. In some implementations, the probabilistic model may include a continuous probability distribution.