Vision Screening Device with Multi-Wavelength Eye Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vision screening devices are limited in their ability to perform a comprehensive range of tests for eye diseases and disorders, and they lack the capability to predict patient behavior effectively, often requiring multiple devices and manual analysis by clinicians.
Innovation Solution
A vision screening device that captures images of the eye under different wavelength ranges of electromagnetic spectrum, using machine learning and artificial intelligence to analyze the images and generate a behavior prediction score, indicating potential violent or unstable behavior, thereby enabling early resource allocation in emergency settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate vision screening devices are used to provide comprehensive testing capabilities, then the range of tests that can be performed is improved, but the device complexity and cost increase
Solution Approach 1:
The patent combines multiple vision screening functions (refractive error testing, visual acuity testing, color vision screening, and behavior prediction) into a single integrated device. The system merges different imaging modalities (infrared, near-infrared, and visible light) and testing capabilities into one unified platform, eliminating the need for multiple separate devices while maintaining comprehensive testing coverage.
Solution Approach 2:
The vision screening device is designed with universal functionality to perform multiple types of vision tests and behavior predictions using a single integrated system. The device can switch between different imaging wavelengths and testing modes to accommodate various vision problems and diagnostic needs, making it a multi-functional platform that replaces several specialized devices.
2Measurement precision
If manual analysis by clinicians is used to evaluate patient behavior, then the accuracy of behavior prediction is improved, but the time required for evaluation and productivity decrease
Solution Approach 1:
The patent replaces manual clinical analysis with an automated machine learning-based system. The machine learning model processes eye images and characteristics to automatically generate behavior prediction scores, eliminating the need for manual clinician evaluation while maintaining prediction accuracy. This substitution of mechanical/manual processes with automated computational systems significantly improves evaluation speed and productivity.
Solution Approach 2:
The system performs self-service by automatically analyzing eye images and generating behavior predictions without requiring manual intervention from clinicians. The machine learning model independently processes the imaging data, extracts relevant features, and produces prediction results, enabling the system to serve itself rather than requiring human expertise for each evaluation.
3Reliability
If traditional blood tests are used to detect patient conditions, then the reliability of diagnosis is improved, but the time required for testing increases
Solution Approach 1:
The patent substitutes traditional blood testing with optical imaging and machine learning analysis. By capturing eye images under multiple wavelengths and using AI to analyze characteristics such as pupil size, eye movement, and retinal features, the system provides rapid diagnosis without requiring time-consuming blood draws and laboratory processing, while maintaining diagnostic reliability.
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
The device provides an automated and efficient method for detecting eye diseases and predicting patient behavior, allowing for timely resource allocation and improved caregiver safety by generating a violence risk score based on eye characteristics, potentially earlier than traditional blood tests.
Implementation Method 1
The device may include components for capturing images of the eye illuminated by radiation of different wavelength ranges of electromagnetic spectrum (e.g., infrared, near-infrared, and visible light)
Implementation Method 2
Some of the vision screening tests require the use of infrared or near-infrared imaging
Data Source
AI summary
A vision screening device for administering vision screening tests to a patient, to determine the presence of diseases and/or abnormalities in the eye(s) of the patient, is described herein. The vision screening device may include associated methods and systems configured to perform the operations of the vision screening tests. The device may include a radiation source configured to generate near-infrared (NIR) radiation, a sensor configured to capture a grayscale image representing the radiation reflected by the eye(s) of the patient, a white light source, and a camera configured to capture a color image of the eye of the patient. The device may also be configured to generate a behavior likelihood score for a patient based on the captured data, the score indicative of a likelihood of particular behavior (e.g., violence, outbursts, withdrawal symptoms, etc.). The device may then recommend action to prepare for such behavior, if necessary.