Chromatic Pupillometry for Objective Brain Function Assessment
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Solution Overview
Problem
Current methods lack objective, reliable, and continuous assessment of brain function for neurological conditions, particularly due to challenges with manual pupillary examinations, such as uncontrolled background light, simplified light responses, and inter-observer discrepancies, which hinder early detection and progression monitoring of brain-related conditions.
Innovation Solution
Utilizing chromatic pupilloperimetry with various wavelengths, intensities, and visual field locations, combined with machine learning algorithms, to analyze pupil light reflex (PLR) for accurate and continuous monitoring of brain function, enabling early detection and assessment of neurological conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual pupillary examination is used, then simplicity of operation is maintained, but measurement precision and reliability deteriorate due to uncontrolled background light, inter-observer discrepancies, and simplified light responses recording
Solution Approach 1:
The patent replaces manual mechanical pupillary examination with an automated digital imaging system using a camera to capture and analyze pupil responses. The system uses computer-controlled light sources with specific wavelengths, intensities, and durations to stimulate the pupil, eliminating manual observation errors and inter-observer discrepancies while maintaining ease of operation through automated processing.
Solution Approach 2:
The patent introduces a camera and image processing software as intermediaries between the light stimulus and the observer. The camera captures precise pupil responses under controlled lighting conditions, and the software automatically analyzes the images to extract pupil size and response parameters, eliminating direct manual measurement errors while preserving operational simplicity.
2Measurement precision
If chromatic pupilloperimetry with multiple parameters and machine learning algorithms is implemented, then diagnostic accuracy and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent creates a multi-functional system where a single device performs multiple functions: it captures images, controls multiple light sources with different wavelengths, processes images to extract various pupil parameters, and applies machine learning algorithms for diagnosis. This universal system achieves high diagnostic accuracy through chromatic pupilloperimetry while consolidating complexity into an integrated platform rather than requiring multiple separate devices.
Solution Approach 2:
The patent implements automated image processing and analysis algorithms that self-correct and self-optimize the measurement process. The machine learning models automatically learn from data to improve diagnostic accuracy, and the system self-regulates light parameters and image capture settings, reducing the need for complex manual configuration and operation.
3Productivity
If continuous real-time monitoring is implemented, then productivity and early detection capability improve, but use of energy and device complexity increase
Solution Approach 1:
The patent implements periodic pupil light response testing where the system captures images at specific time intervals rather than continuously. The light sources are activated in periodic sequences with controlled durations, and the image processing occurs at discrete time points. This periodic approach enables continuous monitoring capability and early detection of neurological changes while significantly reducing energy consumption compared to truly continuous operation.
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 non-invasive, objective, and reliable early detection and monitoring of brain-related conditions, with high diagnostic accuracy, allowing for precise assessment and intervention, especially in acute neurological conditions like stroke and neurodegenerative diseases.
Implementation Method 1
The ipRGCs regulate pupil size through the integration of extrinsic signals from rods and cones and intrinsic signals from melanopsin phototransduction
Implementation Method 2
applying blue and/or red light stimuli to one or more regions of visual field of the eye, the light stimuli configured to induce a response in the pupil
Data Source
AI summary
Provided herein are systems, devices and methods for monitoring the progression of, determining and/or assessing brain related conditions in a subject based on pupil light responses (PLRs) to focal central and peripheral chromatic light stimuli, in particular, by classifying the PLR based on one or more PLR parameter values, wherein the classifying allows monitoring the progression of, determining and/or assessing the brain related condition.