Retinal Protein Deposit Imaging for Noninvasive Disease Detection
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Solution Overview
Problem
Current methods for diagnosing neurodegenerative diseases like Alzheimer's disease are invasive, expensive, and not widely available, lacking a sensitive and specific means for early and accurate detection and quantification of protein deposits in the retina and brain.
Innovation Solution
A method for imaging and classifying protein deposits in the retina using optical techniques, including wide-field imaging, magnification, and machine learning algorithms to differentiate and classify amyloid and other proteins based on their morphology, fractal properties, and interactions with light, enabling detection and severity assessment of neurodegenerative diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive methods (CSF analysis, blood tests, genetic markers) are used for diagnosis, then diagnostic accuracy is improved, but patient comfort and accessibility deteriorate
Solution Approach 1:
The patent replaces invasive mechanical/biological sampling methods (CSF puncture, blood draw) with non-invasive optical imaging methods. Optical coherence tomography (OCT) and adaptive optics imaging systems capture retinal images without physical intrusion, substituting mechanical sampling with light-based detection while maintaining diagnostic capability through retinal biomarker analysis
Solution Approach 2:
The retina serves as an intermediary window to detect brain pathology. Instead of directly sampling brain tissue or fluids, the patent uses the retinal tissue as a surrogate marker system, where retinal protein deposits and structural changes reflect underlying neurodegenerative processes in the brain, enabling indirect but non-invasive diagnosis
2Measurement precision
If brain scans (MRI, PET) are used for detection, then diagnostic sensitivity is improved, but cost and availability deteriorate
Solution Approach 1:
The patent creates a retinal copy or surrogate model of brain pathology. By imaging retinal tissue structure and protein deposits using OCT and adaptive optics, the system produces a diagnostic representation of neurodegenerative changes that mirrors what would be seen in brain tissue, eliminating the need for expensive direct brain imaging while preserving diagnostic information
Solution Approach 2:
The patent employs standard ophthalmic imaging equipment (OCT systems) that is already widely available in clinical settings, replacing expensive specialized brain imaging devices. The retinal imaging approach uses existing, cost-effective technology infrastructure rather than requiring deployment of expensive MRI or PET scanners
3Loss of time
If early detection methods are developed, then diagnostic timing is improved, but measurement complexity deteriorates
Solution Approach 1:
The patent performs preliminary characterization of retinal biomarkers and establishes baseline imaging protocols before clinical deployment. By pre-defining diagnostic criteria for retinal protein deposits, structural changes, and imaging parameters, the system prepares the analytical framework in advance, enabling straightforward early detection without complex real-time analysis
Solution Approach 2:
The patent implements automated image analysis algorithms that provide feedback on retinal imaging data, automatically detecting and quantifying protein deposits and structural changes. The system compares observed retinal features against known disease patterns, providing real-time diagnostic feedback that simplifies the detection process while maintaining high sensitivity for early disease stages
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
Provides a non-invasive, cost-effective, and accurate method for diagnosing neurodegenerative diseases by differentiating and classifying protein deposits in the retina, allowing for early detection and assessment of disease severity in the retina and brain.
Implementation Method 1
performing wide field imaging of the retina using light to illuminate the retina
Implementation Method 2
characterizing a morphology, including size, shape, fractal properties, of the one or more areas of protein or protein deposits
Data Source
AI summary
The present disclosure provides methods and an apparatus for imaging and analysing images of presumed protein deposits in the retina, retinal tissue or retinal structures and discloses methods differentiating or classifying these deposits and other optical signals from retinal structures into 1) whether they contain or do not contain classes, of proteins or protein deposits called amyloids or other proteins and/or protein deposits related to neurodegenerative eye and brain disease(s): 2) which type(s) of amyloid or other proteins or protein deposits they contain, as well as 3) whether the form and/or properties of the deposit are associated with a class of diseases or with one or another specific condition(s) (or disease(s)); whether or not this is a disease or class of disease associated with the retina or more generally with the nervous system, including the brain or 4) classified as associated with one or another level of severity of condition(s), or disease(s).