OCT Intensity Pattern Analysis for Early Retinal Disease Detection
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Solution Overview
Problem
Existing optical coherence tomography (OCT) analysis methods struggle to detect early or subclinical changes in retinal health, leading to delayed diagnosis and ineffective treatment of diseases like age-related macular degeneration (AMD) and multiple sclerosis, as they rely on structural changes that only become visible after significant disease progression.
Innovation Solution
Analyzing OCT data using neural networks trained on intensity patterns and spatial distributions, allowing for early detection and prognosis of retinal health changes by recognizing subtle textural patterns indicative of disease progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCT analysis methods are used to detect structural changes in the retina, then the analysis is simple and straightforward, but the detection timing is delayed until significant disease progression has occurred
Solution Approach 1:
The patent replaces traditional mechanical/structural analysis methods with neural network-based computational analysis. The neural networks process OCT images to detect subtle intensity patterns and textural changes that precede structural modifications, enabling earlier disease detection while maintaining system simplicity through automated processing
Solution Approach 2:
The patent transforms the detection parameters from structural measurements (layer thickness, morphology) to intensity-based parameters (intensity distributions, textural patterns). This parameter transformation allows detection of subclinical changes before structural alterations occur, resolving the timing contradiction
2Measurement precision
If neural network-based analysis is used to detect subclinical changes, then early detection accuracy is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing OCT images to enhance relevant features and preparing training datasets with labeled subclinical changes. This preparation enables the neural networks to efficiently detect early disease markers without requiring excessive computational resources during actual diagnosis
Solution Approach 2:
The patent creates computational models (neural networks) that copy and learn from patterns in training data. These models replicate the detection capability for subclinical changes without requiring direct complex analysis of each new image, reducing real-time processing complexity while maintaining high accuracy
3Loss of time
If traditional structural analysis methods are used, then the analysis process is straightforward, but treatment intervention is delayed until disease progression is significant
Solution Approach 1:
The neural network system performs preliminary detection of subclinical changes before structural damage occurs. By identifying disease markers early in the progression stage, the system enables timely treatment intervention while the retinal structure remains relatively intact, reducing the loss of time for effective treatment
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
The method provides earlier and more accurate assessments of retinal health and disease progression, enabling targeted and timely interventions such as anti-VEGF injections and laser treatments, and predicting conversion from dry to wet AMD with high accuracy.
Implementation Method 1
Many diseases manifest themselves through changes in retinal health. In that manner, diseases that affect tissues in the eye can be diagnosed using optical coherence tomography (OCT) imaging.
Data Source
AI summary
Optical coherence tomography (OCT) data can be analyzed with neural networks trained on OCT data and known clinical outcomes to make more accurate predictions about the development and progression of retinal diseases, central nervous system disorders, and other conditions. The methods take 2D or 3D OCT data derived from different light source configurations and analyze it with neural networks that are trained on OCT images correlated with known clinical outcomes to identify intensity distributions or patterns indicative of different retina conditions. The methods have greater predictive power than traditional OCT analysis because the invention recognizes that subclinical physical changes affect how light interacts with the tissue matter of the retina, and these intensity changes in the image can be distinguishable by a neural network that has been trained on imaging data of retinas.