Ocular Image Analysis System for Disease Phenotyping
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Solution Overview
Problem
Current ocular imaging methods are inadequate for accurately characterizing and quantifying blinding eye diseases, such as Age-related Macular Degeneration (AMD), as they cannot reliably identify patients at risk of progression to late stages or measure disease features effectively, limiting diagnosis, prognosis, and treatment monitoring.
Innovation Solution
A computing system for storing and analyzing ocular images from multiple modalities, including delayed near-infrared analysis (DNIRA), which processes and segments images to generate quantitative data for disease characterization, enabling the identification of disease phenotypes and monitoring changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image-based biomarkers are used for disease characterization, then the diagnostic process is simple and familiar to clinicians, but the measurement precision and ability to characterize complex disease phenotypes is insufficient
Solution Approach 1:
The patent segments ocular images into multiple modalities (en face, cross-sectional, volumetric) and further segments disease features within each modality. This segmentation enables precise characterization of complex phenotypes by analyzing specific regions and features independently, thereby improving measurement precision without requiring a complete system overhaul.
Solution Approach 2:
The patent transitions from traditional 2D image analysis to multi-dimensional analysis by incorporating en face (2D), cross-sectional (1D depth), and volumetric (3D) perspectives. This dimensional expansion allows comprehensive characterization of disease features from multiple angles, significantly improving measurement precision while maintaining manageable system complexity through modular processing.
2Reliability
If multiple imaging modalities are integrated for comprehensive disease analysis, then the diagnostic accuracy and disease characterization improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal analysis platform that processes multiple imaging modalities (OCT, autofluorescence, fundus photography) through a common framework. This multi-functional system handles different modalities using standardized segmentation and analysis algorithms, improving diagnostic accuracy while preventing complexity explosion through methodological unification.
Solution Approach 2:
The patent introduces an intermediary processing layer that standardizes and harmonizes data from different imaging modalities before final analysis. This intermediary layer converts diverse modalities into a unified representation, enabling accurate multi-modality integration without directly managing the full complexity of each individual modality's unique characteristics.
3Measurement precision
If quantitative image analysis is implemented to measure disease extent and progression, then the ability to monitor disease over time and predict treatment response improves, but the difficulty of detecting and measuring disease features increases
Solution Approach 1:
The patent performs preliminary segmentation and classification of disease features before quantitative measurement. By pre-identifying and isolating relevant features (drusen, atrophy, neovascularization) through automated segmentation algorithms, the system simplifies subsequent measurement tasks and reduces the difficulty of detecting subtle progression changes.
Solution Approach 2:
The patent replaces manual measurement and assessment methods with automated computer-based image analysis algorithms. This substitution uses machine learning and pattern recognition to automatically detect, segment, and measure disease features, significantly reducing the difficulty of precise measurement while improving consistency and objectivity.
4Loss of time
If early detection methods are developed to identify patients at risk of progression, then the ability to intervene before blinding occurs improves, but the measurement precision requirements increase to detect subtle early changes
Solution Approach 1:
The patent uses multi-dimensional analysis (en face, cross-sectional, volumetric) to detect subtle early disease changes that may be invisible in traditional 2D views. By examining disease features from multiple dimensions and perspectives, the system can identify early progression risks with higher precision, enabling earlier intervention before blinding occurs.
Solution Approach 2:
The patent segments and isolates subtle early disease features from surrounding normal tissue using automated segmentation algorithms. This segmentation capability allows the system to detect and measure minor changes in disease extent and morphology that would be difficult to perceive in full images, thereby improving early detection precision and reducing time to intervention.
Data Source
AI summary
Disclosed herein are computer systems for, in part, image processing. Also disclosed herein are systems for processing ocular images of multiple imaging modalities to detect ocular diseases. Also disclosed herein are method comprising systems as described herein.