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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 29, 2025

Quality-Controlled Sputum Analysis by Flow Cytometry

Quality-Controlled Sputum Analysis by Flow Cytometry

Published on: August 9, 2021

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or

Yu-Fen Wang^1,2, Jeng-Lin Li³, Chi-Chun Lee³

¹AHEAD Medicine Corporation, San Jose, CA, USA. andrea.wang@aheadmedicine.com.

Methods in Molecular Biology (Clifton, N.J.)

|March 25, 2024

Summary

This study introduces a machine learning method to automate flow cytometry (FC) analysis for hematological diseases. This approach aims to provide faster, more accurate, and reproducible results, addressing expert shortages.

Keywords:

Acute myeloid leukemia Artificial intelligence Automated classification Flow cytometry Machine learning

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Last Updated: Jun 29, 2025

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Published on: January 16, 2019

Area of Science:

Hematology
Immunology
Computational Biology

Background:

Flow cytometry (FC) is crucial for diagnosing and monitoring hematological diseases.
High-dimensional FC datasets present analytical challenges due to complexity and manual gating limitations.
Increasing demand and a shortage of skilled analysts necessitate automated solutions.

Purpose of the Study:

To develop an automated machine learning (ML) method for disease classification using clinical flow cytometry data.
To enable efficient residual disease monitoring in hematological malignancies.
To overcome the limitations of manual, labor-intensive analysis in flow cytometry.

Main Methods:

Implementation of a machine learning algorithm tailored for high-dimensional flow cytometry datasets.
Development of automated classification and monitoring tools.
Utilizing clinical flow data for model training and validation.

Main Results:

The developed ML method facilitates automated disease classification.
The system supports residual disease monitoring.
Potential for increased speed, accuracy, and reproducibility in FC analysis.

Conclusions:

Machine learning offers a viable solution for automating complex flow cytometry data analysis.
This technology can address the challenges of expert shortages and increasing data complexity in hematology.
Automated FC analysis holds promise for improved clinical diagnostics and patient monitoring.