Retinal Fluid Volume Segmentation for OCT Clinical Parameter Prediction
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Solution Overview
Problem
Current methods for characterizing the functional significance of fluid features in diabetic macular edema and other macular vascular disorders are limited, lacking effective tools for quantitative assessment and prediction of disease progression and treatment response.
Innovation Solution
A system and method utilizing optical coherence tomography (OCT) imaging to segment retinal fluid volumes, generate metrics such as retinal fluid indices, and employ machine learning to determine clinical parameters for patients, providing a comprehensive evaluation of retinal health and treatment response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If OCT imaging and segmentation are used to measure retinal fluid volumes, then measurement precision of fluid features is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the retinal tissue into distinct layers and fluid compartments (intraretinal fluid, subretinal fluid) through OCT image analysis. This enables precise measurement of fluid volumes in specific retinal regions while managing complexity through automated image processing algorithms that segment the complex OCT data into manageable anatomical components.
2Measurement precision
If machine learning models are employed to predict clinical parameters from fluid volumes, then prediction accuracy for disease progression is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on large datasets of OCT images and clinical outcomes before actual patient analysis. The models are pre-trained to recognize patterns and relationships between fluid volumes and clinical parameters, enabling rapid prediction during actual clinical use without requiring complex real-time computations.
3Loss of information
If detailed segmentation of retinal layers and fluid volumes is performed, then information completeness about retinal structure is improved, but data processing complexity and computational load increase
Solution Approach 1:
The patent applies the extraction principle by isolating and measuring specific fluid volumes (intraretinal fluid volume, subretinal fluid volume) and retinal layer thicknesses from the complete OCT dataset. Rather than processing all raw image data, the system extracts relevant quantitative metrics such as fluid volumes and structural parameters, reducing processing complexity while maintaining information completeness for clinical assessment.
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
Enables detailed quantitative assessment of retinal features, predicting disease progression and treatment response, thereby improving management of diabetic macular edema and other macular disorders.
Implementation Method 1
An optical coherence tomography (OCT) imager 204 provides a most recent OCT image 302 of the eye 304
Data Source
AI summary
Systems and methods are provided for evaluating an eye using retinal fluid volumes to provide a clinical parameter. An optical coherence tomography (OCT) image of an eye of a patient is obtained. The OCT image is segmented to produce a total retinal volume and one or both of a subretinal fluid volume and an intraretinal fluid volume for a region of interest within the eye. A metric is generated as a function of the total retinal volume and one or both of the subretinal fluid volume and the intraretinal fluid volume. A clinical parameter for the patient is determined from the metric. The determined clinical parameter is provided to a user at a display.