VaNgOGH Descriptor Predicts Anti-VEGF Therapy Response in Diabetic Macular Edema
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Solution Overview
Problem
Current approaches to treating diabetic macular edema (DME) with anti-VEGF therapy lack objective methods to predict treatment response, relying on subjective interpretations of retinal vasculature changes and lacking quantitative biomarkers for distinguishing non-rebounders from rebounders, which affects dosing schedules and treatment efficacy.
Innovation Solution
The use of a vascular network organization via Hough transform (VaNgOGH) descriptor to analyze fluorescein angiography images, identifying differences in vascular phenotypes between non-rebounders and rebounders by computing localized Hough transforms and employing machine learning classifiers to predict treatment response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective physician interpretation is used to evaluate fluorescein angiography images, then clinical judgment can be applied, but measurement precision and objectivity are insufficient
Solution Approach 1:
The patent replaces subjective physician interpretation (mechanical/human system) with automated image processing and machine learning algorithms (computational system). The VaNgOGH descriptor and Hough transform-based vascular network analysis objectively quantify vascular phenotypes, eliminating inter-observer variability while maintaining clinical relevance through trained classifiers that predict treatment response.
Solution Approach 2:
The patent introduces computational intermediaries (image processing algorithms, feature extraction pipelines, and machine learning models) between the raw fluorescein angiography images and clinical decision-making. These intermediaries transform subjective visual assessment into objective quantitative metrics while preserving the diagnostic value of the imaging modality.
2Reliability
If uniform dosing schedules are applied to all patients, then treatment protocol simplicity is maintained, but treatment efficacy varies between non-rebounders and rebounders
Solution Approach 1:
The patent applies local quality by tailoring dosing schedules to individual patient vascular phenotypes rather than using uniform treatment protocols. The VaNgOGH descriptor identifies specific vascular characteristics (tortuosity, branching patterns, density) that correlate with treatment response, enabling personalized dosing intervals matched to each patient's local vascular properties.
Solution Approach 2:
The patent performs preliminary classification of patients as non-rebounders or rebounders using baseline fluorescein angiography analysis before initiating treatment. This preliminary action using the VaNgOGH descriptor and machine learning classifiers allows optimization of dosing schedules in advance, preventing treatment protocol adjustments later and improving overall treatment reliability.
3Productivity
If extended dosing intervals are used for non-rebounders, then resource utilization improves, but identification of non-rebounders requires accurate prediction methods
Solution Approach 1:
The patent performs preliminary classification using baseline fluorescein angiography analysis before treatment initiation, identifying non-rebounders who can safely receive extended dosing intervals. This preliminary action using VaNgOGH-based vascular phenotype analysis ensures accurate patient stratification, enabling resource-efficient extended intervals for predicted non-rebounders while maintaining appropriate frequent dosing for rebounders.
Solution Approach 2:
The patent replaces subjective clinical prediction of treatment response with automated machine learning classifiers that analyze VaNgOGH descriptors. This substitution provides objective, reproducible patient classification with high precision, enabling confident assignment of extended dosing intervals to non-rebounders and optimizing treatment resource allocation.
Data Source
AI summary
Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.