OCT Retinal Scan Analysis Using Reflectivity Profiles
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Solution Overview
Problem
Current Optical Coherence Tomography (OCT) systems face variability in retinal pathology analysis due to differences in acquisition and boundary identification between machines, leading to ambiguous findings and false positive/negative results, especially in non-glaucomatous optic neuropathies and normal optic nerve anatomy.
Innovation Solution
An improved OCT image analysis process that visualizes retinal layers, calculates reflectivity profiles, and uses machine learning algorithms to segment layers and identify lesions, associating disease states with patient data, enabling automated diagnosis and monitoring of retinal diseases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual boundary identification and analysis methods are used in OCT systems, then flexibility and adaptability in analyzing different retinal conditions are maintained, but measurement precision and reliability deteriorate due to variability between machines and operators
Solution Approach 1:
The system performs self-calibration and self-characterization by automatically analyzing OCT images to establish machine-specific reflectivity profiles and layer boundary characteristics without requiring manual operator intervention. The processor independently identifies retinal layers and calculates thickness measurements based on learned patterns from training datasets, enabling the system to correct its own variability and maintain consistent measurements across different OCT machines.
2Reliability
If standardized automated analysis algorithms are implemented across different OCT machines, then measurement precision and reliability improve, but adaptability to handle diverse retinal pathologies and anatomical variations deteriorates
Solution Approach 1:
The system dynamically adjusts analysis parameters and reflectivity profile thresholds based on the specific retinal condition being analyzed. By modifying the training datasets to include diverse pathological conditions and anatomical variations, the system adapts its measurement parameters and boundary identification criteria to suit different clinical scenarios while maintaining standardized automated processing and consistent reliability across machines.
3Adaptability or versatility
If machine learning algorithms are trained on diverse datasets from multiple OCT machines, then the system's ability to handle various retinal conditions improves, but the computational complexity and data processing requirements worsen
Solution Approach 1:
The system segments the analysis process into distinct modules: data preprocessing, reflectivity profile calculation, layer boundary identification, and thickness measurement. By dividing the computational workload and processing different aspects of image analysis separately, the system can handle diverse retinal conditions using specialized algorithms for each condition type while managing computational complexity through modular processing rather than monolithic complex algorithms.
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
Enhances the accuracy and consistency of retinal disease diagnosis by standardizing the analysis of OCT data, reducing variability and improving the detection of lesions and disease states, facilitating longitudinal monitoring of disease progression and therapeutic responses.
Implementation Method 1
Optical Coherence Tomography (OCT) was first introduced as a novel tool for in vivo visualization of retinal layers
Implementation Method 2
calculating reflectivity profiles of various retinal layers of the retina
Data Source
AI summary
The present invention is related to improved methods for analysis of images of the vitreous and/or retina and/or choroid obtained by optical coherence tomography and to methods for making diagnoses of retinal disease based on the reflectivity profiles of various vitreous and/or retinal and/or choroidal layers of the retina.