Remote Retinal Diagnosis System Using Feature Quantification
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Solution Overview
Problem
Current telehealth technologies lack efficient automated clinical decision support for remote medical diagnosis, particularly in analyzing retinal images and providing timely health services to remote locations, with limited ability to quantify features and generate actionable indicators for condition assessment.
Innovation Solution
A system and method utilizing electronic processing devices to receive and analyze retinal image data, quantify features, and generate indicator values for condition assessment, incorporating machine learning algorithms and hybrid communication modalities for remote health services delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated image analysis is implemented, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the retinal image analysis into distinct functional modules: image quality assessment module, feature detection module, and disease grading module. Each module handles specific tasks independently, making the complex automated analysis system manageable and maintainable while improving diagnostic accuracy through specialized processing.
Solution Approach 2:
The patent introduces an intermediary decision support system that bridges the gap between automated image analysis and final diagnostic decisions. This intermediary layer processes automated results, compares them against clinical guidelines, and provides recommendations to healthcare professionals, thereby improving accuracy without requiring the entire system to be overly complex.
2Measurement precision
If machine learning algorithms are used for feature quantification, then condition assessment accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary image quality assessment and feature extraction before applying machine learning algorithms for disease grading. By pre-processing the images to ensure quality and extract basic features, the subsequent machine learning analysis operates on optimized data, reducing processing time while maintaining high accuracy in condition assessment.
Solution Approach 2:
The patent applies machine learning algorithms selectively to specific critical features rather than analyzing every pixel and detail. This partial application of complex algorithms to key diagnostic features maintains high assessment accuracy for important conditions while significantly reducing overall processing time compared to comprehensive analysis of all image data.
3Adaptability or versatility
If remote diagnostic services are expanded to remote locations, then accessibility is improved, but infrastructure requirements increase
Solution Approach 1:
The system is designed with universal functionality that can operate across diverse infrastructure environments. The image processing and analysis capabilities are packaged as software services that can run on various platforms, from basic local devices to cloud servers, enabling remote diagnostic services in locations with varying infrastructure levels without requiring complex dedicated hardware at each site.
Solution Approach 2:
The patent implements a model where complex analysis capabilities are copied and distributed as software modules rather than requiring physical duplication of complex infrastructure. The core analysis algorithms and processing capabilities can be replicated across multiple locations through software deployment, maintaining high diagnostic accuracy while reducing the physical infrastructure burden at remote sites.
Data Source
AI summary
A system for use in remote medical diagnosis of a biological subject, the system including one or more electronic processing devices that receive image data indicative of at least one image of part of the subject's eye from a client device via a communications network, review subject data indicative of at least one subject attribute, select at least one analysis process using results of the review of the subject data, use the analysis process to quantify at least one feature in the image data and generate an indicator value indicative of the quantified at least one feature, the indicator value being used in the assessment of a condition status of at least one condition.