Smartphone Eye Alignment Analysis Using Pixel Intensity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for measuring eye alignment, optical quality, and retinal nerve fiber layer thickness are either manual and cumbersome, lack the ability to detect accommodation, and require expensive, portable instrumentation, making them inaccessible for remote or unsupervised vision screenings.
Innovation Solution
A system and method using a smart device to capture images of the eye under ambient lighting, analyzing pixel intensity differences to determine refractive errors and optical distortions, and transmitting data to a cloud-based network for analysis, allowing for remote and automated assessments by laypersons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual cover test methods are used to measure eye alignment, then measurement capability is provided, but the process is tedious and technically difficult requiring trained professionals
Solution Approach 1:
The patent replaces manual mechanical cover test procedures with an automated image capture and analysis system using a camera and software algorithms to detect eye alignment, eliminating the need for trained professionals to perform manual assessments
Solution Approach 2:
The system enables self-service eye examinations where subjects can perform alignment measurements themselves or with minimal assistance using a smartphone camera and automated software, without requiring trained healthcare providers
2Productivity
If automated eye alignment methods are used, then measurement speed is improved, but the ability to detect accommodation and phoria is lost
Solution Approach 1:
The patent segments the eye examination into multiple distinct measurements including alignment, accommodation, and phoria detection, allowing each parameter to be measured independently and accurately through separate software algorithms analyzing different aspects of the captured images
Solution Approach 2:
The system incorporates feedback mechanisms where the software analyzes captured images to determine accommodation status and adjusts or flags measurements accordingly, providing comprehensive information about eye alignment, focus state, and phoria simultaneously
3Measurement precision
If specialized expensive instrumentation is used to measure retinal nerve fiber layer, then measurement precision is improved, but accessibility and portability are reduced
Solution Approach 1:
The patent uses a smartphone camera to capture images of the retinal nerve fiber layer, creating a digital copy that can be analyzed through software algorithms, replacing expensive specialized instrumentation with a widely accessible device while maintaining measurement capability
Solution Approach 2:
The system enables a single smartphone device to perform multiple eye examination functions including alignment measurement, optical quality assessment, and retinal nerve fiber layer evaluation, making comprehensive eye screening accessible in remote and unsupervised settings
4Measurement precision
If infrared light sources are used in autorefractors, then optical quality detection is improved, but the system requires controlled environments and trained operators
Solution Approach 1:
The patent replaces infrared light-based autorefractor systems with a visible light camera-based system that captures reflected light patterns, eliminating the need for specialized infrared equipment and controlled environments while maintaining measurement accuracy through software analysis
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 efficient, remote, and automated measurement of eye alignment and optical quality, including refractive errors and retinal nerve fiber layer health, without the need for specialized equipment or trained professionals, improving accessibility and accuracy.
Implementation Method 1
detecting, using the computing device, light reflected out of an eye of a subject from a retina of the eye of the subject
Implementation Method 2
the magnitude of the refractive error is determined, and this is often based on the intensity slope of the light (brighter at either the top or the bottom of the pupil) that is reflected off of the retina and back out of the eye
Implementation Method 3
The light returning from this layer is known to be reflected in manner that is polarized. Other existing devices already measure the health of this layer of the retina by measuring the amount of polarized light that is reflected from this layer
Data Source
AI summary
Disclosed herein are methods and systems for making a determination about an eye. The eye is aligned with an image acquisition device using a software application executing on the image acquisition device that provides visual and/or audible indicators through peripherals of the image acquisition device. The acquired image(s) is then transmitted to a cloud-based network where a determination about the eye is made based on the image(s). In some instances, a trained person has an ability to access, review, confirm or modify the determination made about the eye.