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Related Experiment Video

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing.

Rui Tang1, Lin Xia2, Bien Gutierrez2

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA; NanoCellect Biomedical Inc., San Diego, CA, 92121, USA.

Biosensors & Bioelectronics
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, label-free cell sorting system using deep learning for image-activated cell sorting (IACS). The novel system achieves record-low latency and rapid training, making advanced cell analysis more accessible for biomedical research.

Keywords:
AI inferencingArtificial intelligenceImage-activated cell sortingImaging flow cytometry

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Area of Science:

  • Biomedical engineering
  • Computational biology
  • Cell biology

Background:

  • Image-activated cell sorting (IACS) offers insights into biomedical sciences.
  • Deep learning (DL) enhances IACS by classifying cells based on complex morphological features.
  • Current DL-assisted IACS systems have limitations in capability and implementation, hindering widespread adoption.

Purpose of the Study:

  • To develop a novel image-activated cell sorting (IACS) system utilizing fast deep learning algorithms.
  • To achieve label-free cell sorting with significantly reduced latency and training times.
  • To provide a compact, low-cost, and efficient cell sorting solution for diverse biomedical applications.

Main Methods:

  • Implementation of fast deep learning algorithms for cell classification and sorting within an IACS framework.
  • Development of an optimized custom Convolutional Neural Network (CNN) UNet architecture.
  • Utilizing a simple hardware setup comprising an FPGA, PC, and GPU for efficient computation.

Main Results:

  • Achieved an overall sorting latency of less than 3 milliseconds, including signal processing and AI inference.
  • Demonstrated rapid deep learning model training in under 30 minutes for a dataset of 20,000 images.
  • Exhibited high sorting purity: 96.6% for polystyrene beads and 89.05% (monocytes), 92.00% (lymphocytes), and 98.24% (granulocytes) for human leukocytes.

Conclusions:

  • The developed system represents a significant advancement in IACS technology, offering record-breaking speed for AI-driven cell sorting.
  • The system's efficiency, low-cost hardware, and label-free operation make it a practical and accessible tool for various research fields.
  • This innovative approach overcomes previous limitations, paving the way for broader application of DL-assisted cell sorting in biomedical research.