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Related Experiment Video

Updated: Jan 5, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Yibo Zhang^1,2,3, Mengxing Ouyang², Aniruddha Ray^1,2,3,4

¹1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.

Light, Science & Applications

|October 25, 2019

Summary

This summary is machine-generated.

We developed a cost-effective computational cytometer using magnetic forces and AI to detect rare cells in blood. This high-throughput method achieves a 10 cells/mL limit of detection for cancer cell diagnostics.

Keywords:

Biophotonics Imaging and sensing Interference microscopy

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Published on: February 19, 2017

Area of Science:

Biomedical Engineering
Computational Biology
Medical Diagnostics

Background:

Rare cell detection in blood is crucial for disease diagnostics but often limited by high costs and low throughput.
Current methods struggle with sensitivity and speed, hindering widespread clinical application.

Purpose of the Study:

To develop a novel, high-throughput, cost-effective computational cytometer for rare cell detection.
To enhance detection specificity and accuracy using advanced imaging and artificial intelligence.

Main Methods:

Utilized magnetically modulated lensless speckle imaging with oscillatory motion induced by magnetic forces.
Employed lensless time-resolved holographic speckle imaging for rapid 3D detection of magnetically labeled rare cells.
Integrated a deep-learning classifier (densely connected pseudo-3D convolutional neural network - P3D CNN) for automated spatio-temporal feature analysis.

Main Results:

Demonstrated a high-throughput, compact, and cost-effective prototype for detecting MCF7 cancer cells in whole blood.
Achieved a limit of detection (LoD) of 10 cells per milliliter (cells/mL) of whole blood.
Showcased potential for further improvement through multiplexing parallel imaging channels.

Conclusions:

The developed computational cytometer offers a promising solution for sensitive, specific, and efficient rare cell detection.
This technology has significant potential for various biomedical applications, including disease diagnostics and monitoring.
The cost-effectiveness and high-throughput nature make it suitable for broader clinical adoption.