Retinal Image ML Prediction for Early Diabetic Retinopathy Progression
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Solution Overview
Problem
Current methods are inadequate in predicting the manifestation, development, and progression of retinal maladies, particularly diabetic retinopathy, due to the complexity of retinal changes and the lack of effective machine learning models that can accurately forecast these conditions based on retinal images.
Innovation Solution
A method and system using specialized machine learning (ML) models trained on retinal images to predict the development or progression of retinal maladies by categorizing images into distinct types and training models on various retinal image sets, including healthy and pre-clinical retinas, to provide accurate predictions about malady manifestation or severity over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on retinal images to predict retinal malady development, then prediction accuracy is improved, but model complexity and training requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of retinal images with known outcomes before deploying them for prediction. The models are trained in advance on historical retinal images that have been labeled with subsequent malady development information, allowing the system to make accurate predictions without requiring complex real-time analysis during actual use.
2Reliability
If specialized ML models are trained on multiple ordered arrangements of retinal images, then prediction reliability is improved, but training time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into multiple stages: first training on normally ordered retinal images, then training on various permuted and shuffled arrangements of the same images. This segmented approach allows the model to learn robust features that are invariant to image ordering, improving reliability while managing computational resources through structured training phases.
3Adaptability or versatility
If multiple pre-trained ML models are trained on unique retinal image sets, then prediction versatility is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent applies universality by developing a single machine learning model framework that can be trained on different sets of retinal images with various ordering arrangements to predict multiple types of retinal maladies. The same model architecture serves multiple prediction purposes by adapting to different training datasets, eliminating the need for separate specialized models for each malady type or image set.
Data Source
AI summary
A method for making a prediction regarding a malady in a retina of a subject, the method including: (a) receiving at least one retinal image of the retina; (b) calculating a prediction about the retina based on the at least one retinal image of the retina using at least one specialized machine learning (ML) model, the prediction pertaining to whether the malady will develop or manifest or progress in the retina within a time-period.