Digital Pupillometer Imaging for Precise Shape Irregularity Grading
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Solution Overview
Problem
Existing pupillometers lack the capability for precise detection, measurement, and grading of pupillary shape and irregularities, which are crucial for diagnosing neurological conditions and monitoring visual pathway impairments.
Innovation Solution
Development of a pupillometer equipped with a digital camera, microprocessor, and imaging modalities such as speckle and spectral imaging to analyze pupil shape and iris vasculature, capable of detecting neurological disorders like traumatic brain injury and stroke through vascular changes and biomarker detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pupillometers are used, then basic pupil size measurement is achieved, but precise detection and grading of pupillary shape and irregularities cannot be performed
Solution Approach 1:
The pupillary assessment is segmented into multiple independent measurement components: pupil size measurement, shape analysis, irregularity detection, and grading. Each component can be processed separately through the digital camera imaging system and algorithmic analysis, allowing precise shape detection without requiring complete system redesign.
Solution Approach 2:
A digital camera system serves as an intermediary between the traditional pupillometer and the advanced shape analysis requirements. The camera captures high-resolution images that are then processed through specialized algorithms to extract precise pupillary shape information, bridging the gap between simple measurement and complex analysis.
2Measurement precision
If automated shape detection algorithms are implemented, then accurate pupillary shape measurement is achieved, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by capturing high-quality images with proper illumination and positioning before the actual shape analysis begins. The digital camera system pre-processes the image data through noise reduction and enhancement algorithms, so that the subsequent shape detection and grading algorithms work with optimized data, reducing their processing time.
Solution Approach 2:
The algorithm implements partial action by focusing computational resources on the most critical shape features and irregularity indicators. Rather than analyzing every pixel equally, the system identifies and concentrates processing power on key pupillary boundary regions and characteristic shape features, achieving accurate measurement with reduced overall processing time.
3Adaptability or versatility
If multiple imaging modalities are integrated, then comprehensive neurological assessment capability is achieved, but device complexity and cost increase
Solution Approach 1:
The digital camera system is designed with multi-functionality to perform multiple imaging modalities including standard photography, spectral imaging, and speckle imaging. A single device platform supports diverse neurological assessments such as pupillary shape analysis, iris vasculature examination, and cyclotorsion measurement, eliminating the need for separate specialized devices for each function.
Data Source
AI summary
A method for assessing pupillary shape using a pupillometer is described. The method includes capturing sequential images of a subject's pupil; analyzing the images using an image processing algorithm associated with the pupilometer to determine pupil shape characteristics, including ellipticity, eccentricity, and cyclotorsion; using an image processing mode associated with the pupilometer selected from speckle imaging and spectral imaging to enhance visualization of vascular structures with an iris of the pupil; comparing detected pupil shape characteristics against predetermined criteria to assess the presence of abnormalities; and outputting a binary status, graphical representation, or quantified measurement of the pupil shape on a display.