Retinal Image Deep Learning for Geographic Atrophy Prediction
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Solution Overview
Problem
Current methods for tracking geographic atrophy progression in retinal images are inaccurate and unreliable, leading to challenges in identifying effective treatments for geographic atrophy due to variability in manual measurements and wide ranges of lesion growth rates.
Innovation Solution
A computing system utilizing deep learning models trained on various types of retinal images and patient data to predict geographic atrophy progression, incorporating machine learning algorithms such as convolutional neural networks to generate precise predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used to track geographic atrophy progression, then the process is simple and accessible, but the measurement precision and reliability are poor due to variability and inability to detect small changes
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated computer vision system. The system uses deep learning models (U-Net architecture) to automatically segment and measure geographic atrophy lesions from retinal images, eliminating human variability and improving measurement precision while reducing the complexity burden on individual clinicians.
Solution Approach 2:
The patent creates a digital copy of the manual measurement process through automated image processing. By training neural networks on annotated retinal images, the system replicates and enhances the measurement capability, providing consistent, reproducible results that surpass manual methods while maintaining accessibility.
2Reliability
If automated deep learning systems are implemented to improve measurement accuracy, then prediction reliability improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex task of geographic atrophy progression prediction into separate functional modules: image acquisition, automated segmentation using U-Net deep learning models, lesion growth calculation, and prediction generation. This segmentation allows each component to be optimized independently and facilitates easier implementation while maintaining high reliability.
Solution Approach 2:
The patent introduces intermediate processing layers including preprocessed retinal images, segmented lesion masks, and feature extracts that serve as bridges between raw imaging data and final predictions. These intermediaries simplify the overall system architecture by breaking down complex computational tasks into manageable stages.
3Adaptability or versatility
If multiple types of retinal images are processed through separate models, then measurement comprehensiveness improves, but the device complexity and processing time increase
Solution Approach 1:
The patent implements a universal deep learning framework that can process multiple types of retinal images (fundus autofluorescence, infrared reflectance, color) through a single unified architecture. The U-Net model is designed with generic convolutional layers that can adapt to different image modalities, eliminating the need for separate specialized models for each image type while maintaining comprehensive measurement capability.
Data Source
AI summary
An electronic device is disclosed. The device receives retinal images and patient data corresponding to the retinal images. The device can train a first machine learning model (“model”) based on a first group of the retinal images and patient data corresponding to the first group and a second model based on a second group of the retinal images and patient data corresponding to the second group. The electronic device can generate a first prediction based on the first subset of a third group of the retinal images and a second prediction based on the second subset of the third group. After training the first model and the second model, the device can train a third model to predict a geographic atrophy progression in an eye of a patient based on the first and second predictions, the first and second subsets, and patient data corresponding to the first and second subset.