Automated Retinal Image Analysis for Zone 1 Boundary
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Solution Overview
Problem
Current diagnosis of retinopathy of prematurity (ROP) is a manual process prone to subjective judgments, leading to inaccurate determination of zone 1 boundary and less than optimal treatment selections.
Innovation Solution
An automated system and method using a graphical user interface (GUI) for analyzing retinal images to determine vascular distributions and assign zone 1 boundaries, potentially incorporating machine learning and artificial intelligence for consistent and accurate diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual diagnosis process is used, then ease of operation is maintained, but measurement precision deteriorates due to subjective judgments in determining zone 1 boundary
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated image analysis system that uses computer algorithms to objectively determine the zone 1 boundary. The system processes retinal images through defined computational steps, eliminating subjective human judgment while maintaining operational simplicity through a user-friendly interface.
2Measurement precision
If automated system is implemented, then measurement precision improves through objective analysis, but device complexity increases
Solution Approach 1:
The automated system performs self-analysis by automatically processing retinal images and determining zone 1 boundaries without requiring manual intervention. The system independently executes the diagnostic algorithm, generates measurements, and presents results, thereby maintaining ease of operation while achieving high measurement precision through objective computational analysis.
3Reliability
If manual designation of zone 1 boundary is performed, then device complexity remains low, but reliability deteriorates due to considerable variation in manual designation
Solution Approach 1:
The patent transforms the subjective manual parameter estimation into an objective computational process by changing the methodology from human visual assessment to algorithmic image analysis. The system applies consistent mathematical criteria and threshold values to determine the zone 1 boundary, eliminating inter-observer variability and ensuring reliable, reproducible results across different users and cases.
Data Source
AI summary
An automated method for diagnosing and evaluating severity of retinopathy of prematurity in a retina of a patient is provided that is superior to conventional techniques. A graphical user interface (GUI) is provided for receiving biographical information for the patient creating a patient record in a database via the GUI. A photograph of the retina of the patient is collected and placed in the patient record via the GUI. The photograph is then analyzed to determine vascular distributions within the retina. A zone 1 boundary is assigned to the retina based on a set of threshold levels with respect to the determined vascular distributions. A system for performing the automated method is also provided.