Cell Image Analysis and Cell Counts for Automated Disease Differentiation

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Solution Overview

Problem

Conventional disease differentiation methods require complex test steps and skilled examiners, necessitating a more efficient and accessible approach.

Innovation Solution

A disease differentiation support method utilizing a computer algorithm to generate differentiation support information based on first parameters from cell image analysis and second parameters from cell count, including morphological features and cell number analysis, to support disease differentiation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional disease differentiation tests are performed, then differentiation accuracy is maintained, but test complexity and skill requirements increase

Engineering Contradiction:

Improvedisease differentiation accuracyVSAvoidtest procedure complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual examination and complex testing procedures with automated image analysis and computer algorithm-based differentiation. The system captures images of cells and uses automated processing to extract features and perform differentiation, substituting the mechanical/manual system with an automated computational system that maintains accuracy while reducing complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a digital representation (image) of the biological sample and performs differentiation on this copy rather than requiring direct manual examination of the original sample. This allows automated analysis to achieve the same differentiation accuracy as expert manual examination while eliminating the need for skilled operators

Inventive Principle:

Principle #26Copying

2Reliability

If conventional disease differentiation tests are performed, then reliable differentiation results are obtained, but the need for skilled examiners increases

Engineering Contradiction:

Improvedifferentiation result reliabilityVSAvoidoperator skill requirement

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically capturing images, extracting features, and performing disease differentiation without requiring skilled human operators. The computer algorithm independently processes the data and generates differentiation results, making the system self-sufficient and eliminating dependency on expert examiners while maintaining reliability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent substitutes the human expert system with an automated computational system that performs image analysis and differentiation. This replacement maintains the reliability of results through consistent algorithmic processing while completely eliminating the need for skilled human operators

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If automated image analysis is implemented, then operational simplicity is improved, but analysis precision may be reduced

Engineering Contradiction:

Improvetesting process simplicityVSAvoidcell parameter analysis accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent segments the complex task of disease differentiation into distinct automated steps: image capture, feature extraction, parameter calculation, and differentiation decision-making. Each segment is handled by specialized computer algorithms that maintain high precision while collectively providing a simple automated process that requires no manual intervention

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12442749B2Disease differentiation support method, disease differentiation support apparatus, and disease differentiation support computer program

Publication Date: 2025.10.14 SYSMEX CORP

AI summary

Disclosed is a disease differentiation support method for supporting disease differentiation, the disease differentiation support method including: obtaining a first parameter obtained by analyzing an image including a cell contained in a sample collected from a subject; obtaining a second parameter regarding a number of cells contained in the sample; and generating, by using a computer algorithm, differentiation support information for supporting disease differentiation, on the basis of the first parameter and the second parameter.