Corneal Surface Measurement for Machine Ingestion
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Solution Overview
Problem
Current methods for diagnosing and monitoring eye pathologies, such as keratoconus and dry eye, are inefficient and require substantial training, leading to delayed detection and ineffective treatment, especially in pediatric patients and contact lens fitting scenarios.
Innovation Solution
A system and method utilizing an optical mask with apices and edges to capture corneal surface images optimized for machine learning interpretation, allowing for automated analysis and reducing the need for human-interpretable output, thereby simplifying the diagnostic process and enabling non-clinicians to use the technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional corneal measurement devices are designed for clinician interpretation, then the output is interpretable by trained professionals, but the complexity of interpretation and training requirements increase
Solution Approach 1:
The patent creates simplified copies or representations of corneal surface data that are optimized for machine learning algorithms. Instead of using complex clinician-oriented displays, the system generates standardized image formats and feature representations that machines can process directly, thereby reducing interpretation complexity while maintaining diagnostic accuracy through automated analysis
Solution Approach 2:
The patent replaces the mechanical system of human clinician interpretation with an automated machine learning system. By substituting human visual and cognitive processing with computational algorithms, the system eliminates the need for complex training while maintaining or improving diagnostic precision through consistent, objective analysis
2Extent of automation
If machine learning algorithms interpret clinician-designed displays, then automated analysis is enabled, but the algorithms must process suboptimal data formats
Solution Approach 1:
The patent applies preliminary action by preprocessing and transforming corneal measurement data into machine-optimized formats before analysis. The system performs data normalization, feature extraction, and image formatting in advance, so that when machine learning algorithms process the data, it is already in the optimal structure, preventing information loss and improving analysis efficiency
Solution Approach 2:
The patent changes the parameters of data representation from clinician-oriented visual displays to machine-oriented numerical and structural formats. By transforming data into standardized coordinate systems, feature vectors, and optimized image resolutions, the system ensures maximum information retention while enabling effective machine learning processing
3Reliability
If specialized training is required to interpret corneal measurement results, then diagnostic accuracy is maintained, but time commitments and expenses increase
Solution Approach 1:
The patent implements self-service by enabling the system to perform diagnostic interpretation autonomously without requiring trained clinicians to manually analyze each case. The machine learning algorithms automatically process corneal data, generate diagnoses, and provide treatment recommendations, thereby eliminating the need for extensive professional training while maintaining diagnostic reliability through validated algorithms
Solution Approach 2:
The patent creates simplified copies of expert diagnostic reasoning that can be executed by machines. By encoding clinical decision-making rules and patterns into algorithmic form, the system replicates expert-level diagnostic reliability without requiring human experts to be present, thereby reducing training requirements and time commitments
4Quantity of substance
If conventional corneal topography devices are used, then corneal surface data is captured, but the data format is not optimized for automated machine interpretation
Solution Approach 1:
The patent replaces the mechanical data processing workflow with an automated computational system. Instead of requiring clinicians to manually process and interpret complex topography data, the system uses machine learning algorithms to automatically analyze the data, thereby reducing processing complexity while maintaining the quantity and quality of corneal surface information captured
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies the interpretation of ocular surface topography and pathology, reduces computational load, and enables faster detection of conditions like keratoconus and dry eye, improving diagnostic efficiency and reducing training requirements for clinicians.
Implementation Method 1
a light source to transmit incident light through an optical mask
Implementation Method 2
an image sensor to capture data representative of the image that is reflected from the anatomy of a patient
Data Source
AI summary
Methods, systems, and apparatus for illumination of the cornea of an eye are disclosed; in some implementations, such methods, systems, and apparatus may generally comprise or employ a light source and an optical mask, alignment of a viewing portal, and capture of a cornea-reflected image using a camera or other optical array. In some implementations, a system and method may generally comprise or involve generating data that are optimized for ingestion by an artificial intelligence or machine learning engine, thereby facilitating contact lens selection or detection of keratoconus or “dry eye” conditions for ophthalmic, optometric, and pediatric practices. Specifically, the disclosed subject matter teaches innovative cornea-reflective patterns, the reflections of which are designed for ingestion by statistical, machine learning, or artificial intelligence models.