Flow Cytometry Particle Classification Using Dynamic Neural Networks
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Solution Overview
Problem
Current flow-type particle sorting systems face challenges in accurately identifying and sorting particles in a fluid sample due to limitations in detecting and classifying particles based on their light scattering and fluorescence properties, especially when dealing with complex biological samples.
Innovation Solution
The implementation of a dynamic algorithm, such as a machine learning algorithm using an artificial neural network, to detect light from particles in a flow stream, generate data signals, and classify components by creating images from these signals, allowing for the identification and sorting of particles based on their characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional flow cytometry methods are used to detect and classify particles, then the system structure remains simple, but the accuracy of particle identification and classification deteriorates, especially for complex biological samples
Solution Approach 1:
The patent replaces traditional mechanical/electronic classification methods with a machine learning-based system that uses neural networks to process flow cytometry data. The system converts particle detection data into images and uses deep learning algorithms to classify particles, substituting conventional sorting mechanisms with intelligent classification that achieves higher accuracy for complex biological samples.
Solution Approach 2:
The patent creates visual representations (images) of particle data by converting flow cytometry measurements into image formats that can be processed by machine learning models. This copying of data into a different format enables the system to leverage powerful image recognition algorithms to improve particle identification accuracy without directly modifying the physical detection hardware.
2Measurement precision
If dynamic algorithms with machine learning are implemented to improve particle classification, then classification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary processing by converting flow cytometry data into image formats before classification. This pre-processing step organizes and structures the data in a way that machine learning algorithms can efficiently process, reducing the computational burden during actual classification and enabling real-time analysis even with complex algorithms.
Solution Approach 2:
The system dynamically adjusts its processing based on the complexity of the sample and the requirements of the classification task. The machine learning models can adapt their processing depth and computational resources based on the specific particle types and data characteristics, optimizing the balance between accuracy and processing time for different biological samples.
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
This approach enhances the accuracy and efficiency of particle identification and sorting by enabling the classification of particles in real-time, even in complex biological samples, facilitating clinical diagnoses and research applications.
Implementation Method 1
the light scattering and fluorescence properties of the particles are measured
Implementation Method 2
the light scattering and fluorescence properties of the particles are measured
Data Source
AI summary
Aspects of the present disclosure include methods for identifying one or more components of a sample in a flow stream using a dynamic algorithm (e.g., a machine learning algorithm). Methods according to certain embodiments include detecting light from a sample having particles in a flow stream, generating a data signal of parameters of the particles from the detected light, generating an image based on the data signal, comparing the image with one or more image classification parameters and classifying one or more components of the image using a dynamic algorithm that updates the image classification parameters based on the classified components in the image. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided.