AI Wizard for Multispectral Cellular Image Analysis

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Solution Overview

Problem

Current manual analysis of cellular images from biological samples is labor-intensive, prone to bias, and lacks repeatability and standardization, making it inefficient and difficult for large-scale analysis.

Innovation Solution

The AI imaging analysis system combines multispectral imaging flow cytometry with artificial intelligence (AI) algorithms, including convolutional neural networks (CNNs) and random forests, to automatically analyze cellular images, extract features, and classify cell types and morphologies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis of cellular images is performed, then detailed examination and interpretation can be conducted, but the process becomes labor-intensive and time-consuming

Engineering Contradiction:

Improveanalysis accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis with an automated computerized system that captures cellular images, extracts features, and performs classification algorithms. This substitution eliminates human labor while maintaining analysis accuracy through systematic image processing and machine learning techniques.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service analysis by automatically performing image capture, feature extraction, and cell classification without requiring manual intervention. The automated pipeline processes cellular images through multiple analytical stages, allowing the system to serve its own analysis needs independently.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual analysis is used, then flexible interpretation is possible, but repeatability and standardization are compromised

Engineering Contradiction:

Improveinterpretation flexibilityVSAvoidrepeatability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent transforms subjective manual interpretation into objective parameter-based analysis by extracting quantifiable features from cellular images. The system measures specific parameters such as cell morphology, texture, and intensity values, ensuring consistent and repeatable results while maintaining analytical flexibility through configurable classification thresholds.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated AI analysis is implemented, then productivity and consistency are improved, but system complexity increases

Engineering Contradiction:

Improveanalysis throughputVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the complex automated analysis system into distinct functional modules: image capture, feature extraction, and classification. This segmentation allows each component to be optimized independently while working together to achieve high productivity. The modular architecture manages system complexity by organizing tasks into manageable stages.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated analysis system performs multiple functions within a unified platform, including image acquisition, preprocessing, feature extraction, and classification. This multi-functionality increases productivity by eliminating the need for separate manual operations while the integrated design manages complexity through shared resources and standardized interfaces.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If feature extraction and classification algorithms are applied, then analysis objectivity is enhanced, but computational requirements increase

Engineering Contradiction:

Improveanalysis objectivityVSAvoidcomputational energy

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent extracts essential features from full cellular images, isolating the most relevant characteristics for classification. This extraction process reduces the data volume requiring computational analysis while maintaining objectivity by focusing on quantifiable morphological and textural features that directly relate to cell identification.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250131751A1Graphical user interface for artificial intelligence analysis of imaging flow cytometry data

Publication Date: 2025.04.24 CYTEK BIOSCI

AI summary

A user interface wizard generated by AI software walks a user through setting up an artificial intelligence (AI) image analysis experiments on multispectral cellular images using AI image analysis algorithms and AI feature analysis algorithms. In a training experiment mode, a new AI model a new AI model can be trained for a desired experiment on training image cellular data of biological cells in a sample and subsequently to run a classification experiment in a classification mode to classify new image cellular data of biological cells in the sample using the new AI model.