Fellow-Eye Corneal Topography for Early Asymmetry Detection
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Solution Overview
Problem
Current corneal topography systems fail to accurately detect early stages of corneal degenerative diseases and subtle abnormalities due to population-based reference ranges and subjective evaluation, leading to misdiagnosis and potential health disparities, especially in patients seeking surgical correction or those with conditions like keratoconus.
Innovation Solution
A corneal topography device and software that measures and digitally overlays the point-by-point difference between both eyes, generating an elevation difference matrix and colormaps to assess symmetry or asymmetry, using machine learning for improved diagnostic categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If population-based reference ranges are used for corneal topography diagnosis, then diagnostic framework is established, but misdiagnosis and health disparities occur due to demographic variations
Solution Approach 1:
The system uses each patient's fellow eye as an internal reference standard, eliminating the need for external population-based reference ranges. The fellow eye serves itself as the control, automatically adjusting for demographic variations without requiring separate calibration for different populations.
Solution Approach 2:
The invention introduces a new intermediary reference frame based on the patient's own fellow eye topography. This intermediary reference mediates between the measured eye and population norms, providing a personalized baseline that accounts for individual demographic characteristics.
2Measurement precision
If traditional corneal topography metrics are used, then corneal parameters are measured, but subtle abnormalities and early-stage diseases remain undetected
Solution Approach 1:
The invention deliberately introduces asymmetry into the analysis by comparing the measured eye against population norms and identifying deviations from expected symmetry. The system detects subtle abnormalities by measuring asymmetric patterns in elevation, curvature, and thickness that deviate from normal bilateral symmetry.
Solution Approach 2:
The system adds a new dimension to corneal topography by incorporating fellow-eye comparison and population-based reference framing. Instead of analyzing corneal parameters in isolation, it evaluates them within the additional dimension of inter-eye and population variability, enabling detection of subtle abnormalities through multidimensional analysis.
3Ease of operation
If subjective evaluation methods are used for topography analysis, then clinical expertise is utilized, but diagnostic consistency and sensitivity are reduced
Solution Approach 1:
The system implements automated feedback mechanisms that compare measured topography parameters against fellow-eye reference data and population norms. This feedback loop provides objective diagnostic criteria that supplement clinical expertise, improving consistency by automatically highlighting deviations that require attention while maintaining the ease of clinical operation.
4Measurement precision
If fellow-eye comparison is implemented, then diagnostic sensitivity is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system creates a digital copy of the fellow eye's topography data to serve as a reference framework. Instead of requiring complex real-time comparison hardware, it copies the fellow eye measurements and uses this digital twin as the reference standard, simplifying the processing architecture while maintaining high diagnostic sensitivity.
Data Source
AI summary
A device and method for early detection of eye disease including a corneal topography device including a camera, such as a rotating Scheimpflug camera, configured to measure the elevation of a patient's two anterior corneas sequentially or simultaneously; a computer processor; and non-transitory computer readable media including computer readable instructions, which, when executed by the computer processor, causes the device to measure and store elevation data at a plurality of points on the patient's anterior corneal surfaces; organize the elevation data for each cornea into a two-dimensional matrix where the center of the cornea is in the center of the data frame, rotate the data for a first eye 180 degrees around the Y axis relative to a second eye, subtract data on each corresponding corneal point, and generate an elevation difference matrix showing the degree of symmetry or asymmetry between the patient's left and right eye corneal topography.