Ocular Biometric Refractogram for Precise Myopia Progression Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional ocular biometry methods struggle to accurately compare and track changes in ocular biometric parameters over time, leading to inaccurate predictions and management of refractive errors, particularly in the context of myopia progression, due to the non-linear relationship between centiles and visual judgment required in graphical comparisons.
Innovation Solution
A method and system that converts clinically relevant ocular measurements into age-matched normalized parameters, plotted on a single chart (refractogram) using a sigmoid function to accurately calculate centile values, allowing for precise monitoring and management of myopia progression by graphically representing changes in ocular biometric parameters over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional graphical centile charts are used to plot ocular biometric parameters, then visual estimation of centile values can be performed, but measurement precision deteriorates due to visual judgment errors and non-linear relationships between centiles and plotted parameters
Solution Approach 1:
The patent replaces the manual visual estimation method with an automated computational system. A processor automatically calculates centile values by comparing patient measurements against reference population data, eliminating the need for visual judgment on graphical charts. This substitution of mechanical/visual processes with computational algorithms resolves the contradiction by maintaining ease of operation while dramatically improving measurement precision.
Solution Approach 2:
The patent introduces an intermediate computational layer between the raw measurements and the centile determination. Instead of directly reading centile values from graphical plots, the system uses reference population data as an intermediary to calculate precise centile positions through automated comparison and statistical analysis, thereby improving accuracy without complicating the user interface.
2Adaptability or versatility
If multiple separate growth charts are used to track different ocular parameters over time, then comprehensive monitoring can be performed, but device complexity increases and comparison between parameters becomes difficult
Solution Approach 1:
The patent merges multiple separate growth chart functions into a single integrated system. The processor consolidates data from multiple ocular biometric parameters (axial length, corneal radius, lens thickness, etc.) and presents them together with their respective centile values and growth trajectories. This combining approach maintains comprehensive monitoring capability while reducing complexity by providing a unified view rather than requiring separate charts for each parameter.
Solution Approach 2:
The patent creates a universal monitoring system that can handle multiple different ocular parameters through a single interface. The computational model is designed to process various biometric measurements (axial length, corneal curvature, lens parameters) and apply appropriate reference data and centile calculations for each, enabling one system to perform the functions of multiple specialized charts.
3Loss of time
If visual extrapolation is used to predict future ocular measurements from current centile positions, then future values can be estimated, but measurement precision deteriorates due to the non-linear relationship between centiles and underlying parameters
Solution Approach 1:
The patent replaces visual extrapolation methods with computational algorithms for predicting future ocular measurements. Instead of manually extending centile lines on graphs, the processor uses reference population growth patterns and statistical models to calculate future values automatically. This substitution maintains the time-saving prediction capability while significantly improving accuracy by accounting for non-linear relationships through mathematical rather than visual methods.
Data Source
AI summary
A computer implemented system and method for determining and analysing ocular refractive error of an eye. The method determines a set of sample biometric factors for a reference sample of eyes from a set of reference sample physical characteristics. Physical characteristics of a patient's eye are measured such that the type of measured patient physical characteristics include some or all of the reference sample characteristic types. Patient biometric factors are then calculated based on the measured and inherent patient physical characteristics and compared with the sample biometric factors to determine the effect of one or more parameters on the ocular refractive error of an eye. The method may calculate the difference between the refractive contribution of the axial length, cornea and internal optics in the patient's eye and the separate contribution from those factors in the sample physical characteristics.