Automated Pupillary Response Evaluation via Composite Image Analysis
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Solution Overview
Problem
Current methods for evaluating pupillary responses to light stimuli, such as the Swinging Flashlight Test, lack specificity and sensitivity due to reliance on manual observation, leading to inaccurate detection of ocular dysfunctions and inability to distinguish between different ocular disorders.
Innovation Solution
A system and method utilizing computing devices to alternately expose both eyes to light stimulation, concurrently capture and process image data of both eyes to create composite images for evaluating pupillary responses, allowing for precise measurement of afferent and efferent optic nerve pathways and detection of ocular dysfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation methods (Swinging Flashlight Test) are used to evaluate pupillary responses, then the examination process is simple and requires minimal equipment, but the measurement precision and sensitivity are insufficient leading to inaccurate detection of ocular dysfunctions
Solution Approach 1:
The patent replaces manual mechanical observation with an automated image capture and processing system. A computing device captures sequential images of both pupils and automatically analyzes pupillary responses to light stimuli, eliminating the need for manual measurement and significantly improving detection accuracy while maintaining clinical practicality.
Solution Approach 2:
The patent uses digital image copying to capture and store pupillary responses. By creating digital copies of the pupils through image capture, the system enables precise measurement and analysis without physically interfering with the natural pupillary reflex, allowing for accurate detection of subtle differences in pupillary responses.
2Measurement precision
If a single bright light source is used to stimulate both eyes simultaneously, then the test procedure is simple, but the sensitivity and specificity are reduced due to inability to observe small differences in reflexes between eyes
Solution Approach 1:
The patent segments the light stimulation by presenting light to each eye alternately rather than simultaneously. This allows the system to capture and compare pupillary responses to light presented to the left eye versus the right eye, enabling detection of subtle asymmetries in pupillary reflexes that would be imperceptible with simultaneous stimulation.
Solution Approach 2:
The patent employs periodic alternation of light stimulation between the two eyes. By systematically switching which eye receives light stimulation in a periodic manner, the system can accumulate multiple measurements and statistically analyze differences in pupillary responses, greatly enhancing sensitivity while maintaining operational simplicity through automated control.
3Loss of information
If the Swinging Flashlight Test is performed with pendular light movement between eyes, then the test can be performed quickly, but the examiner cannot observe both pupils simultaneously leading to subjective errors and loss of information
Solution Approach 1:
The patent replaces the examiner's subjective visual comparison with automated image analysis. The computing device objectively measures pupillary dimensions from captured images and compares responses between eyes, eliminating information loss due to human visual limitations and subjective judgment errors.
Solution Approach 2:
The system provides automated feedback by analyzing captured images and generating objective measurements of pupillary responses. This feedback loop allows the system to automatically detect and quantify differences in pupillary reflexes between eyes, preventing information loss that occurs when examiners rely on subjective visual assessment.
4Measurement precision
If Neutral Density filters are used to quantify RAPD by nullification exercise, then a quantitative measurement can be obtained, but the procedure becomes complex and small differences less than 0.3 Log units cannot be measured due to filter limitations
Solution Approach 1:
The patent replaces the mechanical filter-based nullification method with automated image analysis. By capturing images and computationally analyzing pupillary responses, the system can detect and quantify extremely small differences in pupillary reflexes without being constrained by discrete filter steps, achieving superior measurement precision.
Solution Approach 2:
The system changes the measurement parameter from filter density (discrete Log units) to direct pupillary dimension measurement (continuous pixel-based measurement). This allows detection of arbitrarily small differences in pupillary responses, overcoming the 0.3 Log unit limitation of traditional filter methods.
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 system provides highly sensitive and discriminative measurements of pupillary reflexes, enabling accurate detection of ocular dysfunctions, including glaucoma and other disorders, by simultaneously evaluating direct and consensual reflexes and differentiating between various ocular pathologies.
Implementation Method 1
a handheld, very bright light source is shined first into one eye of the patient and then into the other eye
Implementation Method 2
concurrently capturing, with at least one image device controlled by the at least one computing device, image data of the first eye and the second eye during the exposing
Data Source
AI summary
Solutions for evaluating the pupillary responses of a patient are disclosed. An illustrative method includes alternately exposing a first eye and a second eye of the patient to light stimulation in successive intervals, the light stimulation provided by at least one light source controlled by at least one computing device; concurrently capturing, with at least one image device controlled by the at least one computing device, image data of the first eye and the second eye during the exposing; and using the at least one computing device to perform the following: determine a center point of the first eye within the image data of the first eye and a center point of the second eye within the image data of the second eye; obtain image data of a first half of the first eye having an edge defined by a line of pixels intersecting the determined center point of the first eye; obtain image data of a second half of the second eye, the second half of the second eye opposing the first half of the first eye and having an edge defined by a line of pixels intersecting the determined center point of the second eye; create a composite image including the image data of the first half of the first eye and the image data of the second half of the second eye; and provide the composite image for evaluation.