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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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Updated: Jul 22, 2025

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

Artificial Intelligence for Clinical Flow Cytometry.

Robert P Seifert¹, David A Gorlin², Andrew A Borkowski³

¹Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, 4800 Southwest 35th Drive, Gainesville, FL 32608, USA.

Clinics in Laboratory Medicine

|July 22, 2023

Summary

This summary is machine-generated.

Machine learning in flow cytometry shows promise but faces challenges. Key issues include "black box" algorithms hindering explainability and potential poor generalizability without collaborative development.

Keywords:

Artificial intelligence Clinical flow cytometry Flow cytometry Hematopathology Machine learning

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Last Updated: Jul 22, 2025

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

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Area of Science:

* Computational Biology
* Data Science
* Immunology

Background:

* Machine learning (ML) offers advanced analytical capabilities.
* Flow cytometry generates complex, high-dimensional datasets.
* Integrating ML into flow cytometry workflows is an emerging research area.

Purpose of the Study:

* To review fundamental machine learning principles.
* To explore recent applications of ML in flow cytometry.
* To identify challenges and limitations of ML in this field.

Main Methods:

* Review of current literature on machine learning applications in flow cytometry.
* Discussion of the principles behind machine learning algorithms.
* Analysis of the strengths and weaknesses of existing ML models for flow cytometry data.

Main Results:

* ML applications in flow cytometry are promising for data analysis.
* Explainability of ML models (the "black box" problem) is a major barrier to adoption.
* Poor generalizability of ML models is a concern without multi-institutional development.

Conclusions:

* Overcoming the explainability challenge is crucial for widespread ML adoption in flow cytometry.
* Collaborative, multi-institutional efforts are needed to improve the generalizability of ML models.
* Future directions include the deployment of augmented decision-making tools.