Serum-Agglutination Test Imaging for Objective Sample Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing serum-agglutination tests for infectious diseases are often performed manually, requiring subjective interpretation and are prone to errors due to operator dependence, and existing automated systems lack reliability in feature extraction and analysis.
Innovation Solution
An apparatus that automatically analyzes images of microplate samples using a processing unit to extract features such as blob detection, closed contours, and circular/elliptical geometry, enabling reliable classification of sample positivity through a combination of these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interpretation of serum-agglutination tests is performed, then operational simplicity is maintained, but measurement precision and reliability deteriorate due to subjective interpretation and operator dependence
Solution Approach 1:
The patent replaces the manual visual interpretation mechanism with an automated image processing system. The processing unit captures images of the microplate wells and automatically analyzes agglutination patterns using computer vision algorithms, eliminating subjective human interpretation while maintaining operational simplicity through automated workflows
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the serum-agglutination reaction and the final interpretation. This intermediary captures the visual result through imaging and processes it algorithmically, providing an objective bridge between the biological reaction and the diagnostic conclusion
2Measurement precision
If automated image analysis is implemented, then measurement precision and reliability improve, but device complexity increases due to additional processing requirements
Solution Approach 1:
The processing unit performs multiple functions: it captures images, processes them through various analysis algorithms (blob detection, contour analysis, geometric feature extraction), and generates diagnostic interpretations. This multi-functionality consolidates what could be separate complex systems into a single integrated unit, managing complexity while delivering precise measurements
Solution Approach 2:
The patent transforms the interpretation task from subjective visual assessment to objective quantitative parameter extraction. By converting agglutination patterns into measurable features (blob count, contour properties, geometric characteristics), the system achieves high precision through parameter-based analysis rather than qualitative judgment
3Reliability
If multiple image features are extracted and combined for classification, then reliability improves through more deterministic analysis, but processing complexity increases
Solution Approach 1:
The patent segments the image analysis into distinct feature extraction components: blob detection, closed contour identification, and geometric shape analysis. Each segment extracts specific characteristics independently, and their results are combined for final classification. This segmentation makes the complex processing manageable and reliable through modular analysis
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus for performing serum-agglutination tests is described, the apparatus having a housing area for housing a plate with a plurality of wells containing a sample to be analyzed, a processing unit, and at least one image detector in communication with the processing unit for acquiring images of the plate located in the housing area. The processing unit comprises instructions for processing the acquired images which, when executed, cause said processing unit to carry out the following steps: controlling the acquisition of at least one image containing at least one well, extracting a plurality of features from the image, said features comprising at least one number of aggregates of pixels detectable in said image, a number of closed contours detectable in said image, and a number of elements having a determined at least partially circular and/or elliptical geometry detectable in said image; and classifying, based on a combination of said features, the sample contained in the well, providing information on the positivity of said sample through said classification.