Head-Mounted VR Ptosis Measurement With Eye-Tracked Visual Field Mapping
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Solution Overview
Problem
Current methods for measuring superior visual field loss in ptosis patients are time-consuming, costly, and inaccurate, and existing VR testing modalities do not effectively integrate eye tracking cameras to assist physicians in margin to reflex distance measurement.
Innovation Solution
A head-mounted VR system with integrated eye-tracking cameras uses a Median Cut Hemifield Test algorithm to rapidly and accurately measure superior visual field loss by calculating the area under the eyelid meridian, incorporating real-time adjustments based on eye tracking predictions and modular ophthalmic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual field examination methods (HVFA perimetry) are used, then measurement accuracy is improved, but testing time increases significantly (15-60 minutes) and cost increases
Solution Approach 1:
The visual field test is segmented into specific columns corresponding to anatomical regions (first column for 0-10 degrees, second column for 10-20 degrees, third column for 20-30 degrees). This segmentation allows targeted testing of the superior visual field affected by ptosis without requiring a complete comprehensive perimetry, thereby reducing testing time while maintaining accuracy for the specific clinical question.
Solution Approach 2:
The system performs partial perimetry by testing only the necessary columns (first, second, and third columns) rather than the complete visual field. This partial action approach provides sufficient data for ptosis severity assessment and surgical decision-making without the time cost of a full comprehensive test.
2Reliability
If Humphrey Visual Field Analyzer is used, then measurement reliability is improved, but device cost and technician requirement increase
Solution Approach 1:
The system incorporates automated eye tracking that self-adjusts to the patient's eye position and pupil characteristics, eliminating the need for manual alignment and technician intervention. The automated detection and tracking of eye movements and pupil centers enables the system to self-calibrate and maintain reliable measurements without requiring a certified technician to operate the equipment.
Solution Approach 2:
The system replaces the mechanical manual perimetry process with automated digital imaging and computational algorithms. Instead of manual stimulus presentation and patient response recording, the system uses captured eye images and automated detection algorithms to determine visual field boundaries, reducing device complexity and eliminating technician requirements while maintaining measurement reliability.
3Ease of operation
If VR testing modalities are used, then patient engagement is improved, but integration of eye tracking cameras for MRD measurement is insufficient
Solution Approach 1:
The system merges the VR perimetry interface with integrated eye tracking camera functionality into a single unified system. The eye tracking cameras are incorporated within the VR headset structure, allowing simultaneous acquisition of visual field stimulus responses and eye movement data. This merging enables the system to perform both patient engagement through immersive VR testing and precise MRD measurement through integrated eye tracking, eliminating the need for separate measurement devices.
Data Source
AI summary
A method to predict visual field loss includes defining a superior visual field as rows and columns in a coordinate system. The method further includes performing a display and record process for a first column of the columns by: initiating a base condition, initiating a recurrence condition, and initiating a stop condition when a difference between the new pivot point and a previous pivot point is less than a predetermined threshold. The method further includes repeating the display and record process for additional columns of the columns. The method further includes defining an eyelid meridian based on recorded coordinates for a set of points that are visible to the patient, calculating an area of the superior visual field based on the eyelid meridian, and determining the visual field loss based the area of the superior visual field.