Automated Pupillary Light Reflex Analysis for Neurodevelopmental Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pupillary measurement techniques for assessing neurodevelopmental disorders and traumatic brain injuries lack specificity and quantitative output, and existing devices are inadequate in terms of storage solutions and algorithmic analysis capabilities.
Innovation Solution
A computer-implemented method utilizing a centralized server and cloud storage system for data collection and analysis of pupillary light reflex (PLR) data, including user interface modifications for calibration and interaction, anonymization of patient data, and generation of risk assessments for neurodevelopmental disorders such as autism spectrum disorder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personal measurement techniques are used for pupillary assessment, then the method is simple to perform, but the measurement precision and quantitative output are insufficient
Solution Approach 1:
The patent replaces manual mechanical measurement techniques with an automated digital imaging and processing system. The system uses a camera to capture pupillary images and automatically processes them through algorithms to extract quantitative measurements, eliminating the need for manual measurement while providing precise numerical output.
Solution Approach 2:
The system performs self-measurement and self-analysis through automated image capture and processing. The software automatically detects pupillary boundaries, calculates diameter measurements, and generates risk assessments without requiring manual intervention, enabling the system to serve itself in the measurement process.
2Measurement precision
If quantitative pupillary detection devices are developed, then measurement precision is improved, but device complexity and lack of storage/analysis capabilities worsen
Solution Approach 1:
The system is designed as a multi-functional integrated platform that combines pupillary measurement, data storage, algorithmic analysis, and risk assessment generation in a single device. The system can perform multiple functions including capturing images, storing patient data, processing measurements, and generating clinical reports, eliminating the need for separate devices for each function.
Solution Approach 2:
The patent merges the measurement device, storage system, and analysis software into a single integrated unit. The camera, database, processing algorithms, and user interface are combined in one system, simplifying the overall device architecture while maintaining comprehensive functionality for pupillary assessment.
3Measurement precision
If comprehensive data collection and analysis are implemented, then diagnostic accuracy is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary data processing and analysis automatically as data is collected. Algorithms continuously process pupillary measurements in real-time, pre-calculating risk assessments and generating intermediate results during the data collection phase, so that comprehensive analysis is already partially complete before final review is needed.
Solution Approach 2:
The system provides real-time feedback during data collection by continuously analyzing pupillary measurements and updating risk assessments. This feedback mechanism allows clinicians to see preliminary results during the assessment process, reducing the perceived processing time and enabling immediate clinical decision-making.
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 solution provides a more accurate and quantitative assessment of neurodevelopmental disorders, enabling early detection and predictive analysis, improving patient compliance, and facilitating comprehensive diagnostic evaluations by integrating data from multiple sources and providing actionable insights for healthcare providers.
Implementation Method 1
collecting a data set from one or more data collection devices; and determining, via the data set and by the light levels, based at least in part by the pupillary change in measurement of the subject
Data Source
AI summary
Neurological abnormalities are often discovered through observation by health care providers, and/or parent report. Many neurodevelopmental disorders such as ASD are purely identified through behavioral analysis, and cannot be screened for using a biomarker or quantitative stimulus-response test. Current screening tools contain subjective components based on parent report and clinician observation, vary in consistency of use across providers, and demands resources, knowledge, and access to skilled expertise. As a result, the only tests used today require lengthy and subjective behavioral analysis and often, miss or misidentify neurodevelopmental disorders contributing to a delayed diagnosis. The technology disclosed herein allow for a solution to this systemic problem.