Fundus Image ML Model for Diagnostic Accuracy
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Solution Overview
Problem
Current medical diagnosis systems rely heavily on multiple data sources and are not efficient in analyzing the health of a patient using only fundus images, lacking the ability to accurately predict medical condition progression, effective treatments, and risk evaluation.
Innovation Solution
A fundus image processing machine learning model that processes one or more fundus images to generate health analysis data, including predictions on medical condition presence, progression, treatment effectiveness, and risk assessment, while providing insights into the prediction process through attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are used for medical diagnosis, then diagnostic accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on a single, highly informative data source (fundus images) rather than processing multiple data sources. By taking out the most discriminative feature (retinal vasculature patterns from fundus images) and using it as the primary diagnostic input, the system achieves high diagnostic accuracy while reducing system complexity and data processing requirements.
2Measurement precision
If comprehensive patient data is collected for analysis, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant information from patient data - specifically fundus images containing retinal vasculature patterns. By eliminating the need to process comprehensive patient data including demographics, medical history, and laboratory results, the system achieves rapid processing while maintaining high prediction accuracy through focused analysis of the most discriminative features.
Solution Approach 2:
The system performs preliminary action by capturing fundus images that inherently contain rich diagnostic information about cardiovascular health, diabetes, and other conditions. This preliminary capture of critical data in a single non-invasive image eliminates the need for subsequent extensive data collection and processing, reducing overall processing time while maintaining comprehensive diagnostic capability.
3Measurement precision
If detailed analysis of fundus images is performed, then health analysis accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the fundus image into meaningful regions (retinal vasculature, optic disc, macula) and analyzing each region separately using specialized algorithms. This segmentation approach enables detailed analysis of specific anatomical structures and their pathological changes while reducing overall computational complexity by focusing processing power on relevant regions rather than analyzing the entire image uniformly.
Solution Approach 2:
The system implements local quality by applying different analysis methods and levels of detail to different regions of the fundus image. Critical regions such as the retinal vasculature receive more intensive analysis with higher computational resources, while less critical areas receive lighter processing. This localized approach maintains high health analysis accuracy for diagnostically important features while reducing overall computational complexity.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.