Automated Agglutination Analyzer Contour Comparison
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Solution Overview
Problem
Current methods for detecting agglutination in medical diagnostics are labor-intensive, prone to human subjectivity, and lack standardization, leading to variability in titer values across different laboratories and readers.
Innovation Solution
The implementation of advanced digital imaging and processing techniques to analyze agglutination reactions, including contour comparison processes, which automatically differentiate between agglutinated, non-agglutinated, and non-specific inhibition states, and utilize quality control plates for standardization and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expert readers manually analyze each 96 well plate to determine agglutination, then subjective differentiation between opaque buttons and transparent solutions is achieved, but labor intensity increases and variability between readers occurs
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical detection system that captures images of the 96-well plate and uses image processing algorithms to automatically identify agglutination patterns. The system substitutes human eyes and brain processing with a camera-based imaging system and computational analysis, eliminating subjectivity while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the plate analysis to be performed automatically without requiring expert human readers. The automated image capture and processing system independently identifies agglutination events, determines titer values, and generates results without human intervention in the actual measurement process.
2Reliability
If advanced digital imaging and contour comparison techniques are implemented for automated analysis, then standardization and repeatability improve, but device complexity and initial labor requirements increase
Solution Approach 1:
The system standardizes agglutination detection by transforming visual patterns into quantifiable image parameters such as optical density, area, and contour characteristics. By converting subjective visual assessment into objective numerical measurements, the system enables consistent analysis across different laboratories and operators, with contour comparison algorithms providing repeatable results.
Solution Approach 2:
The patent introduces digital imaging as an intermediary between the agglutination reaction and the analysis process. Instead of direct human observation, the system captures optical images that serve as intermediaries, allowing computational algorithms to analyze and compare contour patterns objectively. This intermediary layer standardizes the detection process across different settings.
3Measurement precision
If automated image processing with contour comparison is used to differentiate agglutination states, then detection accuracy and objectivity improve, but processing time and computational requirements increase
Solution Approach 1:
The system segments the image analysis into distinct processing stages: image capture, preprocessing, contour detection, parameter extraction, and comparison with reference patterns. By dividing the complex analysis into modular segments, the system efficiently processes images while maintaining high accuracy in differentiating agglutination states.
Solution Approach 2:
The system performs preliminary actions by capturing images at optimized time points and pre-processing them with appropriate filters and enhancements before contour analysis. Reference contour patterns are pre-established for different agglutination states, allowing rapid comparison and classification during actual analysis, thereby reducing processing time.
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
This approach enables automated, standardized, and repeatable detection of agglutination, reducing errors and subjectivity, and providing accurate titer values, thereby improving efficiency and consistency across laboratories.
Implementation Method 1
an optical source and an optical detector. The optical detector to detect light from the optical source and that passes through the plurality of reaction vessels and the plurality of openings
Implementation Method 2
processing the digital image to generate a processed image, wherein the step of processing comprises defining a plurality of areas of the processed image corresponding to each of the plurality of reaction vessels and each of the areas correspond to a plurality of pixels; measuring within each of the defined plurality of areas a pixel intensity for each of the plurality of pixels positioned within the area
Implementation Method 3
Cells or particles which are not agglutinated, referred herein as non-agglutinated, settle under the force of gravity to the bottom of a vessel, with the settled cells visually observable as a dark region or button on the bottom
Data Source
AI summary
The systems and methods contained herein are directed toward automated analysis of agglutination reactions to determine properties of materials, including viruses and vaccines thereto. Advanced digital imaging and processing techniques are used to determine the presence or absence of viruses or antibodies within a fluid sample. The systems and methods are versatile, and can be used to determine specific properties of biomaterials and viruses, such as titer value, concentration, genotype, phenotype, serotype, vaccine efficacy, viral resistance and other properties of relevance in the medical, research and development fields. Also provided are systems and methods of standardization, repeatability, and data storage and transmittal to reduce errors and subjectivity inherent to conventional assays characterized by human readers.