VR Visual Pattern Testing for Early Macular Degeneration Detection
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Solution Overview
Problem
Current VR technology lacks the capability to accurately diagnose and monitor ocular conditions such as macular degeneration, tear film disorders, retinal disorders, and sleep-related disorders, despite advancements in eye-tracking technology.
Innovation Solution
Implementing a VR system equipped with high-resolution headsets, advanced eye-tracking sensors, and sophisticated software algorithms to conduct detailed visual tests and analyze eye movements, pupil responses, and tear film dynamics for early detection and monitoring of these conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for detecting macular degeneration, then the diagnostic process is simple and quick, but the detection precision and ability to identify early signs are insufficient
Solution Approach 1:
The diagnostic process is segmented into multiple distinct visual tests (grid pattern detection, dot arrangement recognition, color differentiation tasks) that can be administered sequentially through the VR system. Each test targets specific aspects of macular function, allowing comprehensive assessment while maintaining manageable complexity through modular test design.
Solution Approach 2:
The patent transitions from traditional two-dimensional paper-based Amsler grids to immersive three-dimensional virtual reality environments. This dimensional enhancement allows for more sophisticated visual pattern presentation and eye movement tracking, significantly improving detection precision for early macular degeneration signs.
2Reliability
If VR technology with eye-tracking sensors is implemented, then the detection precision and comprehensiveness improve, but the device complexity and cost increase
Solution Approach 1:
The VR headset serves multiple functions: it displays visual stimuli for macular testing, tracks eye movements through integrated sensors, captures pupil responses, and monitors tear film dynamics. This multi-functionality consolidates what would otherwise require separate diagnostic devices into a single integrated system, improving reliability while managing complexity through consolidation.
Solution Approach 2:
The system performs self-calibration and automated data analysis, reducing the need for manual intervention and specialized equipment. The software automatically processes eye-tracking data, compares results against normative databases, and generates diagnostic reports, thereby improving reliability without proportionally increasing operational complexity.
3Measurement precision
If detailed visual patterns and multiple tests are administered, then the diagnostic accuracy and early detection capability improve, but the examination time and patient burden increase
Solution Approach 1:
Multiple visual tests are administered continuously in sequence without requiring the patient to reposition or reset between tests. The VR system maintains continuous eye-tracking and data collection throughout the examination, allowing comprehensive assessment to be completed in a single uninterrupted session, thereby reducing overall examination time while maintaining high diagnostic accuracy.
Solution Approach 2:
The examination protocol is dynamically adapted based on real-time patient responses and performance. If early signs of macular degeneration are detected in initial tests, the system automatically adjusts subsequent test parameters and focuses assessment on affected areas, optimizing the balance between diagnostic thoroughness and examination duration.
Data Source
AI summary
A patient's visual health can be evaluated via a virtual reality (VR) system, which can include a VR headset in electronic communication with a computing device. The computing device can cause a phenomenon (such as a visual test or an image) to be displayed on the screens of the VR headset. Using varying combinations of sensors, cameras, probes, and microphones, the VR headset collects data about the patient as she perceives and responds to the phenomenon. Optionally, the computing device can alter the phenomenon and analyze the patient's perception and responses to evaluate the patient for macular degeneration. Optionally, the computing device can alter the phenomenon based on the patient's perception and responses.