Visual Acuity Modeling for Chart-Invariant Vision Tracking
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Solution Overview
Problem
Current visual acuity testing methods lack precision and consistency across different chart designs, leading to imprecise comparisons and tracking of vision changes, especially in clinical settings, and fail to provide quantitative results.
Innovation Solution
Development of personalized acuity charts and algorithms that generate chart-specific and chart-invariant metrics, using Bayesian adaptive testing to optimize optotype selection and scoring, adhering to design standards, and enabling precise, rapid visual acuity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple different acuity chart designs are used in clinical practice, then testing accessibility and ease of use are improved, but measurement precision and consistency across different charts deteriorate
Solution Approach 1:
The patent creates a universal scoring algorithm that can process and score data from multiple different acuity chart designs (ETDRS, Snellen, Tumbling E, Landolt C, etc.) using a single unified approach. This allows the same algorithm to universally handle diverse chart types while maintaining consistent measurement precision across all of them, resolving the contradiction between testing accessibility and measurement consistency.
Solution Approach 2:
The patent transforms acuity measurements from chart-specific qualitative results to chart-invariant quantitative parameters expressed in logMAR units. By changing the parameter representation from discrete chart line numbers to continuous logarithmic acuity values, the system enables precise comparison and coordination across different chart designs while maintaining ease of use.
2Ease of operation
If traditional chart-based testing is used, then ease of use and widespread distribution are improved, but test resolution and precision deteriorate
Solution Approach 1:
The patent introduces a computer-based scoring algorithm as an intermediary between the traditional chart testing process and the final acuity measurement. This intermediary layer captures detailed response data from patients' interactions with the chart (which optotypes they identified, in what order, confidence levels), then processes this data to generate precise quantitative results, thereby enhancing the resolution of traditional chart testing while maintaining its ease of use.
3Measurement precision
If clinical trial standards are used for acuity testing, then measurement precision is improved, but testing time and complexity increase
Solution Approach 1:
The patent extracts the essential precision-enhancing elements from complex clinical trial protocols and incorporates them into a simplified scoring algorithm. By taking out only the critical components (detailed response tracking, psychometric function fitting, confidence-based scoring) while eliminating unnecessary complexity, the system achieves clinical trial-level precision in a time-efficient manner suitable for routine clinical practice.
4Ease of operation
If qualitative acuity results are provided, then ease of interpretation is improved, but ability to track vision changes deteriorates
Solution Approach 1:
The patent adds a quantitative dimension to traditional qualitative acuity results by expressing measurements in continuous logMAR units rather than discrete chart line numbers. This dimensional transformation enables precise tracking of small vision changes over time while maintaining interpretability through the familiar logMAR scale, thereby resolving the contradiction between ease of interpretation and ability to track vision changes.
Data Source
AI summary
Disclosed herein are system and method for testing and analysis of visual acuity and changes using an acuity model, the acuity model generated based on one or more acuity chart design parameters and candidate acuity parameters calculated using the acuity test data of the subject, the acuity model comprising a chart-specific psychometric function determined using a family of multiple-optotype psychometric functions, and wherein the acuity model is configurable to estimate possibility of obtaining the acuity test data of the subject.