Retinal Image HTNR Detection Using Vessel Segmentation and Severity Scoring
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Solution Overview
Problem
Traditional diagnostic approaches for hypertensive retinopathy (HTNR) rely on labor-intensive, subjective expert examination of retinal fundus images, lacking efficiency and consistency.
Innovation Solution
An AI-driven system utilizing deep learning techniques, including convolutional neural networks (CNNs) and U-Net architectures, for automated HTNR detection through preprocessing, vessel segmentation, and marker detection, generating a severity score based on retinal image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert examination of retinal fundus images is used, then diagnostic accuracy can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of manual expert examination with an automated deep learning system comprising convolutional neural networks (CNNs) and U-Net architectures. The system automatically performs vessel segmentation, marker detection, and severity scoring, eliminating the need for manual image interpretation while maintaining diagnostic accuracy and significantly improving screening efficiency
Solution Approach 2:
The deep learning system enables self-service automation where the algorithm independently processes retinal images through preprocessing, segmentation, and detection stages without human intervention. The system generates severity scores and diagnostic outputs autonomously, allowing high-volume screening to occur without requiring expert ophthalmologists for each case
2Ease of operation
If manual interpretation of retinal images is performed, then subjective assessment can be conducted, but consistency and reproducibility are compromised
Solution Approach 1:
The patent transforms subjective diagnostic parameters into objective, quantifiable metrics through the deep learning system. Vessel caliber, tortuosity, and marker presence are measured as specific numerical parameters rather than subjective impressions. The system outputs standardized severity scores based on consistent parameter thresholds, ensuring reproducible results across different patients and time points
Solution Approach 2:
The system replaces the variable human judgment mechanism with a consistent algorithmic decision-making process. The deep learning models apply the same segmentation and detection criteria uniformly to all images, eliminating inter-observer variability and ensuring that diagnostic consistency is maintained across the entire patient population
3Measurement precision
If comprehensive retinal image analysis is performed manually, then detailed detection of vascular changes is possible, but the process becomes highly dependent on clinician experience
Solution Approach 1:
The patent segments the complex diagnostic task into distinct modular components: preprocessing, vessel segmentation, retinal marker detection, vascular marker detection, and severity scoring. Each module is handled by specialized deep learning models (CNNs and U-Net), allowing comprehensive analysis to be performed through systematic processing of multiple image features without requiring holistic expert judgment
Solution Approach 2:
The system replaces the expert-dependent analysis mechanism with an automated multi-model deep learning system. The CNNs and U-Net architectures collectively perform all aspects of image analysis including detecting subtle vascular changes, identifying retinal markers, and assessing severity, thereby eliminating dependence on individual clinician experience while maintaining high detection sensitivity
4Productivity
If automated deep learning systems are implemented, then screening efficiency and accessibility are improved, but system complexity increases
Solution Approach 1:
The patent segments the automated system into distinct functional modules with specialized models for each task: preprocessing, vessel segmentation (U-Net), retinal marker detection (CNNs), vascular marker detection (CNNs), and severity scoring. This modular architecture allows the complex system to be built from manageable components, facilitating implementation and maintenance while achieving high screening throughput
Solution Approach 2:
The deep learning system is designed with multi-functional capabilities where the same architectural framework (CNNs and U-Net) handles multiple detection tasks including vessel segmentation, retinal marker identification, and vascular abnormality detection. This universal approach reduces overall system complexity compared to having separate specialized systems for each function
Data Source
AI summary
A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and/or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and/or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.