Eye-Movement Imaging System for Objective Strabismus Assessment
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Solution Overview
Problem
Existing strabismus assessment methods, such as corneal light reflex test and prism cover test, are subjective and prone to variations due to examiner experience and patient cooperation, leading to inconsistent results.
Innovation Solution
A system and method utilizing AI-based image processing and an automated occluder with near-infrared filters to objectively assess ocular misalignment by capturing eye movements during visual stimulus observation, incorporating deep learning for precise strabismus deviation determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods (corneal light reflex test, prism cover test) are used, then examiner flexibility and patient adaptability are maintained, but measurement precision and reliability deteriorate due to subjectivity and examiner experience variations
Solution Approach 1:
The patent replaces manual mechanical assessment methods with an automated image-based assessment system. The processing module captures images of the patient's eyes and automatically determines ocular misalignment conditions, substituting the examiner's manual observation and measurement with computational image analysis, thereby eliminating subjectivity while maintaining assessment capability
Solution Approach 2:
The system enables automated self-assessment by having the processing module independently analyze captured eye images and determine ocular misalignment conditions without requiring examiner intervention. The system processes images, detects eye positions, and generates assessment results autonomously, reducing dependency on examiner experience and skill
2Reliability
If automated image processing is implemented, then measurement precision and repeatability are improved, but device complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing multiple eye images at different time points before final analysis. The processing module pre-processes these images, extracting key features and preparing data for misalignment determination, which streamlines the final assessment and reduces overall processing time
Solution Approach 2:
The assessment process is segmented into distinct stages: image capture, image processing, feature extraction, and misalignment determination. This segmentation allows parallel processing of multiple images and operations, improving efficiency and reducing total assessment time while maintaining reliability
3Measurement precision
If multiple images are captured for analysis, then measurement precision is improved, but loss of time and processing complexity increase
Solution Approach 1:
The system maintains continuous useful action by capturing multiple eye images in rapid succession without interruption. The processing module continuously processes these images, analyzing eye position changes over time, which provides robust measurement data while maintaining high assessment throughput through uninterrupted imaging and processing
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
Provides accurate, repeatable, and efficient strabismus assessment with reduced subjectivity, enabling precise measurement of ocular deviation for clinical management.
Implementation Method 1
The automated occluder includes a near-infrared (NIR) filter
Implementation Method 2
the image capturing module comprises an infrared illumination source arranged to illuminate the observer eyes
Data Source
AI summary
A system for strabismus assessment and a method of strabismus assessment. The system comprises a display module arranged to display a visual stimulus to be observed by an observer; an image capturing module arranged to record a plurality of images of at least one eye of the observer observing the visual stimulus; and a processing module arranged to determine an ocular misalignment condition of the observer based on the plurality of images recorded by the image capturing module.