Retinal Vessel Plaque Detection via Blood Flow Velocity Discrepancy
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Solution Overview
Problem
Current methods for early detection of hypertensive retinopathy lack effectiveness in identifying asymptomatic patients at risk of stroke and fail to provide timely preventive treatment, as they do not accurately assess blood flow velocities in retinal vessels, which are crucial for diagnosing plaque affected vessels.
Innovation Solution
A system and method that utilize a blood flow velocity estimation model learned from healthy retinal images to compare estimated and actual blood flow velocities in retinal vessels, detecting candidate plaque affected vessels by identifying discrepancies, and further analyzing these vessels using 3D optical spectral domain tomography imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for early detection of hypertensive retinopathy, then the detection process is simple, but the detection accuracy is insufficient and cannot accurately identify asymptomatic patients at risk of stroke
Solution Approach 1:
The retinal vessel is divided into multiple vessel fragments along its length. Each fragment is independently analyzed for blood flow velocity characteristics, allowing localized detection of plaque-related flow disturbances that would be missed in overall vessel analysis
Solution Approach 2:
A blood flow velocity estimation model is introduced as an intermediary to generate expected velocity values, which are then compared with actual measured velocities. This intermediary enables quantitative detection of discrepancies indicating plaque presence without requiring direct visualization of the plaque itself
2Reliability
If blood flow velocity estimation model is used to detect plaque affected vessels, then early detection accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The blood flow velocity estimation model is pre-trained on a large cohort of healthy individuals before actual detection. This preliminary action creates a ready-to-use reference model that enables rapid comparison with patient data, reducing real-time processing requirements while maintaining high detection reliability
Solution Approach 2:
The system analyzes only specific vessel fragments where discrepancies are detected, rather than processing entire retinal vasculature uniformly. This partial action approach focuses computational resources on high-risk areas, reducing overall processing time while maintaining detection sensitivity
Data Source
AI summary
An embodiment of the invention receives by an interface a retinal image from a patient, and identifies by a feature extraction device vessel fragments in the retinal image. The vessel fragments include at least a portion of a major vessel and at least a portion of a branch connected to a major vessel. A processor computes estimated blood flow velocities in the vessel fragments with a blood flow velocity estimation model and determines actual blood flow velocities in the vessel fragments. An analysis engine compares the actual blood flow velocities in the vessel fragments to the estimated blood flow velocities in the vessel fragments. The analysis engine detects a candidate plaque affected vessel fragment when the estimated blood flow velocities in the vessel fragments differs from the actual blood flow velocities in the vessel fragments by a predetermined amount.