Automated Eye Evaluation Using Multi-Modal Imaging Feature Integration
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Solution Overview
Problem
Current medical decision support systems for ophthalmic evaluations, particularly for retinal and choroidal vascular diseases, lack the ability to integrate multiple imaging modalities and clinical variables effectively, leading to inefficiencies in diagnostic accuracy and therapeutic decision-making.
Innovation Solution
A system that combines features from multiple imaging modalities, such as OCT and angiography, with clinical demographics to provide an integrative analytics model for automated diagnosis and individualized therapeutic decision support, using pattern recognition classifiers to assign clinical parameters and select optimal therapeutic interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple imaging modalities are integrated, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple imaging modalities (OCT, angiography, fundus photography) into a unified analysis system that processes images from different sources simultaneously. The system integrates structural information from OCT, vascular information from angiography, and contextual information from fundus photography to create a comprehensive diagnostic assessment, thereby improving diagnostic accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The system performs multiple diagnostic functions using a single integrated platform. It simultaneously extracts features from different imaging modalities, performs pattern recognition across multiple data types, and provides both diagnostic classification and therapeutic recommendations, making the system universally applicable to various retinal diseases while reducing the need for separate specialized systems.
2Productivity
If automated pattern recognition is implemented, then diagnostic efficiency is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary feature extraction from imaging data before pattern recognition classification. By pre-processing the images to extract relevant features (such as vascular patterns, structural abnormalities, and textural characteristics) before the main classification step, the system reduces the computational burden during real-time diagnosis while maintaining high diagnostic efficiency.
Solution Approach 2:
The diagnostic process is divided into distinct segments: image acquisition from multiple modalities, feature extraction from each modality, integration of extracted features, pattern recognition classification, and therapeutic recommendation. This segmentation allows computational tasks to be distributed and optimized at each stage, improving overall efficiency while managing computational requirements through modular processing.
3Reliability
If individualized therapeutic recommendations are provided, then treatment effectiveness is improved, but analysis complexity increases
Solution Approach 1:
The system provides individualized therapeutic recommendations by analyzing local characteristics of each patient's specific disease presentation. Rather than applying generic treatment protocols, the system examines patient-specific features such as disease location, severity, vascular patterns, and structural abnormalities to tailor treatment recommendations to the unique characteristics of each case, thereby improving treatment effectiveness.
Solution Approach 2:
The system incorporates feedback mechanisms where pattern recognition results inform therapeutic recommendations, and treatment outcomes can be fed back into the system for continuous improvement. The classification results and diagnostic assessments provide feedback that guides the selection and adjustment of therapeutic interventions, creating a closed-loop system that enhances treatment effectiveness while managing analysis complexity through iterative optimization.
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
This approach significantly enhances diagnostic efficiency, improves screening platforms, and facilitates personalized treatment choices by integrating complex interactions between imaging features and clinical variables, thereby improving diagnostic accuracy and therapeutic outcomes for conditions like diabetic retinopathy and macular degeneration.
Implementation Method 1
Optical coherence tomography is an interferometric technique, typically employing near-infrared light. The use of relatively long wavelength light allows it to penetrate into the scattering medium.
Implementation Method 2
Optical coherence tomography is an optical signal acquisition and processing method that captures micrometer-resolution, three-dimensional images from within optical scattering media, such as biological tissue.
Data Source
AI summary
Systems and methods are provided for evaluating an eye of a patient. A first imager interface receives a first image of at least a portion of the eye generated via a first imaging modality, and a second imager interface receives a second image of at least a portion of the eye generated via a second imaging modality. A first feature extractor extracts a first set of numerical features from the first image, with one feature representing a spatial extent of one of a tissue layer, a tissue structure, and a pathological feature. A second feature extractor extracts a second set of numerical features from the second image, with one feature representing one of a number and a location of vascular irregularities within the eye. A pattern recognition component evaluates the first plurality of features and the second plurality of features to assign a clinical parameter to the eye.