Automated Whole Blood Analysis via Digital Imaging and Fluorescence
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Solution Overview
Problem
Current methods for performing a leukocyte differential count (LDC) on whole blood samples are labor-intensive, time-consuming, and reliant on operator skill, with limitations in accuracy and versatility, particularly due to the need for diluted samples and laborious blood smears.
Innovation Solution
An apparatus and method for analyzing whole blood samples within a chamber, using imaging and fluorescence techniques to identify and quantify features of white blood cells, such as monocytes, eosinophils, and neutrophils, through the use of colorants and programmable analysis, allowing for automated and accurate LDC without dilution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual blood smear analysis is used, then operator flexibility is maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical blood smear preparation and microscopic examination with an automated digital imaging system. The apparatus captures images of whole blood samples and uses computer algorithms to identify and classify white blood cells, eliminating the need for manual smear preparation while maintaining diagnostic accuracy and enabling high-throughput analysis.
Solution Approach 2:
The system enables self-service analysis by automating the entire leukocyte differential count process. The apparatus automatically captures images, processes them through algorithms that identify cell types based on morphological features, and generates results without requiring operator intervention for each sample, thereby increasing productivity while preserving flexibility through programmable analysis methods.
2Extent of automation
If flow cytometry is used, then automation is achieved, but sample dilution and fluid handling equipment are required
Solution Approach 1:
The patent extracts the essential function of white blood cell identification from the complex flow cytometry system. By removing the need for sample dilution and fluid handling equipment, the invention uses direct digital imaging of undiluted whole blood samples, capturing cellular images and using image processing algorithms to achieve automation without the cumbersome fluid handling requirements of flow cytometry.
Solution Approach 2:
The system replaces the mechanical fluid handling and serial cell passage requirements of flow cytometry with a static digital imaging approach. Whole blood samples are placed in a chamber and imaged directly, with automated analysis performed through computer vision algorithms that identify and classify white blood cells based on their visual characteristics, eliminating the need for complex fluid handling equipment.
3Measurement precision
If traditional LDC methods are used, then white blood cell identification is achieved, but operator skill level significantly affects accuracy
Solution Approach 1:
The system incorporates feedback mechanisms where the automated image analysis algorithm continuously refines its cell identification and classification based on captured images. The programmable analyzer uses established algorithms to objectively measure cellular features and provide consistent, reproducible results that are independent of operator skill level, while allowing for validation and adjustment of results by trained personnel.
Solution Approach 2:
The patent replaces the subjective human judgment required in traditional LDC with objective computer-based image analysis. The system captures high-resolution images of white blood cells and uses algorithms to automatically identify cell types based on morphological features, eliminating the variability introduced by different operator skill levels while maintaining high diagnostic accuracy.
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 enables high-accuracy, automated analysis of whole blood samples, reducing operator dependency and time, while providing greater versatility and accuracy in distinguishing different types of white blood cells, as demonstrated by empirical data and comparison with manual hematological analysis.
Implementation Method 1
using imaging and fluorescence techniques to identify and quantify features of white blood cells
Data Source
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AI summary
A method and apparatus for identifying one or more target constituents (e.g., white blood cells) within a biological sample is provided. The method includes the steps of: a) adding at least one colorant to the sample; b) disposing the sample into a chamber defined by at least one transparent panel; c) creating at least one image of the sample quiescently residing within the chamber; d) identifying target constituents within the sample image; e) quantitatively analyzing at least some of the identified target constituents within the image relative to one or more predetermined quantitatively determinable features; and f) identifying at least one type of target constituent within the identified target constituents using the quantitatively determinable features.