Retinal Scan Screening System for Early Disease Detection
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Solution Overview
Problem
Many individuals avoid routine eye exams due to cost, scheduling difficulties, and lack of accessibility, leading to undiagnosed eye diseases such as macular degeneration, glaucoma, and diabetic retinopathy, which can result in vision loss and blindness if not detected early.
Innovation Solution
A convenient ophthalmic testing system (OTS) using artificial intelligence and machine learning to screen for ophthalmic disorders, allowing for immediate, real-time feedback and easy accessibility through kiosks in various locations, which can analyze retinal images to detect diseases like glaucoma and diabetic retinopathy, and provide users with visual guides to their results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eye exams are conducted at specialized clinics, then diagnostic accuracy is improved, but accessibility and convenience deteriorate
Solution Approach 1:
The patent deploys retinal imaging devices in accessible locations such as retail stores, creating copies of specialized diagnostic capabilities outside traditional clinics. These distributed imaging devices capture retinal images that are then analyzed by centralized AI systems, bringing eye exam accessibility to communities without requiring travel to specialized facilities.
Solution Approach 2:
The patent introduces an AI-based image analysis system as an intermediary between the distributed retinal imaging devices and specialized ophthalmologists. The AI algorithm pre-screens retinal images for pathological conditions, triaging cases before they reach specialist review. This intermediary enables accurate screening at accessible locations while maintaining diagnostic quality through centralized expert validation.
2Loss of information
If comprehensive eye exams are performed, then detection of systemic diseases is improved, but cost and time investment increase
Solution Approach 1:
The patent segments the comprehensive eye exam process into two distinct stages: (1) automated retinal image capture and AI-based screening for both ocular and systemic conditions, and (2) selective follow-up by ophthalmologists only for cases requiring expert evaluation. This segmentation allows systematic disease screening to occur rapidly at accessible locations, with time-intensive specialist consultation reserved only for necessary cases.
Solution Approach 2:
The patent implements self-service retinal imaging kiosks that autonomously capture retinal images and perform AI-based analysis without requiring physician presence. The system automatically screens for diabetic retinopathy, hypertensive retinopathy, and other systemic conditions, providing preliminary results immediately. This self-service capability eliminates scheduling delays while maintaining comprehensive screening for systemic diseases.
3Measurement precision
If AI algorithms are trained extensively for accurate diagnosis, then diagnostic precision is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the diagnostic workflow into AI-based screening for common conditions and human expert evaluation for complex or ambiguous cases. The AI algorithm is trained to confidently diagnose clear-cut cases of diabetic retinopathy, hypertensive retinopathy, and other systemic conditions, reserving specialist review only for uncertain or atypical presentations. This segmentation reduces overall computational resource requirements while maintaining high diagnostic precision.
Solution Approach 2:
The patent adjusts the confidence threshold parameter of the AI algorithm to optimize the balance between automated diagnosis and specialist review. By setting appropriate probability cutoffs, the system maximizes accurate automated diagnoses for routine cases, reducing computational load while maintaining diagnostic precision. Cases falling below the confidence threshold are automatically referred for specialist evaluation.
Data Source
AI summary
The present disclosure provides for an ophthalmic testing system that may prescreen for ophthalmic diseases and systemic diseases. In some embodiments, an OTS may provide general screening results and relatives scores to patients that may be at risk for diabetic retinopathy, macular degeneration, or Alzheimer's. In some implementations, the OTS may offer self-screening features without collecting personal information that may identify the individual. In some embodiments, the OTS may use artificial intelligence and machine learning and machine learning to provide accurate and instant analysis and results to a user.