Ocular Imaging Prognosis Using Multimodal Eye Model Alignment
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Solution Overview
Problem
Current ophthalmic imaging techniques suffer from limited resolution, field of view, and poor contrast, making it difficult to diagnose eye conditions consistently and predict treatment responses accurately, particularly in diseases like AMD, where progression and treatment variability among patients are significant.
Innovation Solution
Combining different types of ocular imaging data, such as MRI, OCT, and OCTA, to generate a comprehensive model of the eye, enhancing anatomical features and applying coordinate metadata for alignment, and using neural networks to analyze these data for diagnosis and prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current imaging techniques are used, then imaging speed and ease of operation are maintained, but diagnostic accuracy and measurement precision deteriorate due to limited resolution, field of view, and poor contrast
Solution Approach 1:
The patent combines multiple imaging modalities (OCT, fundus photography, MRI, ultrasound) into a single integrated ocular model. This merging of different imaging techniques allows the system to overcome the limitations of individual modalities by providing comprehensive anatomical, physiological, and functional information simultaneously, thereby improving diagnostic accuracy without requiring multiple separate imaging sessions or systems.
Solution Approach 2:
The integrated ocular model serves multiple diagnostic and prognostic functions within a single system. It can analyze anatomical structures, assess physiological functions, predict disease progression, and evaluate treatment responses across various eye conditions (AMD, glaucoma, diabetic retinopathy). This multi-functionality allows a single complex system to replace multiple specialized imaging systems while providing comprehensive patient care.
2Measurement precision
If multiple imaging modalities are combined to improve diagnostic accuracy, then measurement precision improves, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs artificial intelligence and machine learning algorithms as intermediaries to process and integrate data from multiple imaging modalities. These AI systems automatically align, register, and synthesize data from OCT, fundus photography, MRI, and ultrasound, transforming complex multi-modal data into a unified ocular model. This intermediary processing layer manages the computational complexity while delivering accurate diagnostic insights to clinicians.
Solution Approach 2:
The system creates a virtual digital copy of the patient's eye by integrating data from multiple imaging modalities into a comprehensive ocular model. This digital twin or virtual model replicates the anatomical and physiological characteristics of the patient's eye, allowing clinicians to analyze and predict disease progression without requiring physical manipulation of multiple imaging systems simultaneously. The virtual model serves as a consolidated representation that simplifies clinical interpretation.
3Measurement precision
If comprehensive ocular modeling is implemented to predict treatment response, then prognostic accuracy improves, but loss of time in data collection and processing increases
Solution Approach 1:
The patent performs preliminary data collection and processing by creating the comprehensive ocular model at baseline before treatment begins. The integrated model captures anatomical, physiological, and functional data from multiple modalities in advance, establishing a detailed pre-treatment snapshot. This preliminary modeling allows clinicians to predict treatment response and personalize treatment plans before intervention, eliminating the need for repeated extensive imaging during early treatment evaluation and reducing overall time investment.
Data Source
AI summary
A method for prognosing disease in an eye, the method including: receiving present eye image data representative of the eye at a present time; analyzing the present eye image data using a neural network to generate a prediction for future eye image data representative of the eye at a future time; and generating a prognostic parameter for the eye from the future eye image data, wherein the neural network is trained using past eye image data representative of a plurality of eyes at first and second past times to generate one or more eye image data change functions, wherein the eye image data change function is applied by the neural network to the present eye image data to generate the prediction for the future eye image data.