AI-Gated Particle Sorting Using Deep Learning Inference
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Solution Overview
Problem
Existing image-activated cell sorting (IACS) systems face limitations in real-time data processing and sorting due to computational power constraints and hardware limitations, leading to inefficiencies and inaccuracies in particle classification and sorting.
Innovation Solution
The implementation of an AI-based gating model using deep learning algorithms and convolutional neural networks (CNNs) for real-time image-activated particle sorting, optimized with a UNet CNN autoencoder model and accelerated by FPGA and GPU processing, enables fast and accurate particle classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing methods are used for particle sorting, then system complexity is reduced, but sorting speed and accuracy deteriorate due to computational power constraints
Solution Approach 1:
The patent replaces traditional mechanical/optical sorting systems with an AI-based computational system. Deep learning models process particle images and determine sorting decisions, substituting complex optical-mechanical sorting mechanisms with intelligent algorithms that run on programmable hardware, thereby increasing sorting speed while managing system complexity through software-based control
Solution Approach 2:
The patent transforms the sorting decision-making process from fixed threshold-based parameters to dynamic AI-generated parameters. The system adjusts classification criteria based on learned features from training data, enabling adaptive sorting that improves accuracy and speed by optimizing decision parameters in real-time based on particle characteristics
2Measurement precision
If real-time image processing is implemented for particle sorting, then sorting accuracy is improved, but processing time increases due to computational requirements
Solution Approach 1:
The patent implements offline training of deep learning models using extensive particle image datasets before deployment. This preliminary action pre-learns classification patterns and features, enabling the system to make rapid real-time sorting decisions without performing heavy computational training during actual sorting operations, thus achieving high accuracy with minimal processing time
Solution Approach 2:
The patent extracts and separates the computationally intensive model training process from the real-time sorting operation. Training is performed offline and independently, while the sorting system uses the pre-trained model for rapid inference. This extraction allows the real-time system to focus only on lightweight prediction tasks, maintaining high accuracy while minimizing processing time
3Manufacturing precision
If high-resolution imaging is used for particle analysis, then sorting precision is improved, but throughput decreases due to data processing burden
Solution Approach 1:
The patent extracts only the most relevant features from high-resolution particle images using deep learning convolutional networks. Instead of processing entire high-resolution images, the system identifies and extracts key morphological and textural features that are critical for sorting decisions, thereby maintaining high sorting precision while reducing the data processing burden to preserve throughput
Data Source
AI summary
Disclosed are systems, devices and methods for imaging and image-activated sorting of particles in a flow system based on AI gating. In some aspects, a system includes a particle flow device to flow particles through a channel, an imaging system to obtain image data of a particle during flow through the channel, and a control command unit to produce a control command for sorting the particle based on an AI-based gating model and the image data, and an actuator to direct, according to the control command, the particle into one of a plurality of output paths of the particle flow device in real-time.