AI Imaging Flow Cytometry Cell Classification
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Solution Overview
Problem
Existing imaging flow cytometry technologies face challenges in efficiently analyzing and classifying biological cells using both brightfield and fluorescent imaging modes, often requiring manual intervention and being prone to bias and subjectivity.
Innovation Solution
The implementation of an artificial intelligence (AI) system that combines machine learning algorithms, such as convolutional neural networks (CNNs) and random forests, with imaging flow cytometry data to automatically analyze and classify cell images, reducing the need for manual analysis and enhancing objectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis is used for cell classification, then flexibility and adaptability are maintained, but analysis time increases and objectivity decreases
Solution Approach 1:
The system uses AI algorithms that automatically analyze and classify cell images without requiring manual intervention. The deep learning models self-train on imaging flow cytometry data, performing classification tasks autonomously while maintaining high objectivity and accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated AI-based image processing. Convolutional neural networks and other machine learning algorithms substitute human analysts, providing consistent, objective classification results while dramatically reducing analysis time.
2Productivity
If AI algorithms are implemented for automated classification, then analysis speed and objectivity improve, but computational complexity increases
Solution Approach 1:
The patent segments the computational task into distinct processing stages: image acquisition, pre-processing, feature extraction, and classification. Different AI algorithms are applied at each stage, with simple filters for pre-processing and more complex models like CNNs for classification, managing computational complexity through task division.
Solution Approach 2:
The system applies multiple layers of AI processing with varying degrees of complexity. Simple image processing operations are performed first, followed by more computationally intensive classification only where needed, optimizing the balance between analysis speed and computational resource usage.
3Measurement precision
If multiple imaging modes are combined for analysis, then classification accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges data from multiple imaging modes (brightfield and fluorescent channels) into a unified analysis framework. The AI system processes and integrates information from different imaging modalities simultaneously, using the complementary data to improve classification accuracy while managing processing complexity through coordinated multi-channel analysis.
Data Source
AI summary
A multispectral imaging flow cytometer acquires a variety of images in different imaging modes, such as brightfield, side scatter, and a plurality of fluorescent images of a different moving biological cells in a sample fluid. These images can be processed by a plurality of artificial intelligence algorithms and/or machine learning tools executed by a processor, a neural engine, a neural processor, or a convolutional neural network (CNN). Deep learning analysis of the images can be performed with the CNN on the images to extract image features. Feature data can be extracted about the moving biological cell as well. An AI algorithm, such as random forest algorithm, can use both the image features of a cell and the feature data of the cell to classify the biological cell as to its type.