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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: Aug 31, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM

Julien Demagny¹, Camille Roussel², Maïlys Le Guyader³

¹Univ. Picardie Jules Verne, HEMATIM UR4666, F80025, Amiens, France; Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.

|August 20, 2022

Summary

A novel method combines imaging flow cytometry (IFC) and artificial intelligence for accurate, automated schistocyte quantification. This approach offers a reliable, operator-independent tool for diagnosing thrombotic microangiopathy syndrome (TMA) in emergency settings.

Keywords:

Imaging flow cytometry Machine learning Schistocyte Thrombotic microangiopathy

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Last Updated: Aug 31, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Area of Science:

Hematology
Medical Diagnostics
Computational Biology

Background:

Schistocyte counts are crucial for diagnosing thrombotic microangiopathy syndrome (TMA).
Manual schistocyte quantification is complex and time-consuming.
Existing automated methods have limitations impacting their clinical utility.

Purpose of the Study:

To develop and validate a novel method for direct, label-free, and operator-independent schistocyte quantification.
To combine imaging flow cytometry (IFC) with artificial intelligence (AI) for enhanced diagnostic accuracy.
To provide a reliable tool for urgent TMA diagnosis.

Main Methods:

Utilized 135,045 IFC images from 14 patients.
Extracted features using IDEAS® software and a convolutional neural network (CNN) with Keras.
Trained a support vector machine (SVM) classifier for schistocyte quantification.

Main Results:

CNN-derived features achieved higher accuracy (94.03%) than IDEAS® features (91.54%).
Combined features yielded the best accuracy (95.64%).
Excellent correlation (0.93) with expert hematologists was observed, with high sensitivity (100%) and specificity (91.3%) for detecting schistocytosis.

Conclusions:

IFC combined with AI provides a reliable and operator-independent method for schistocyte quantification.
The method requires no pre-analytical processing, making it ideal for emergency situations like TMA diagnosis.
This innovative approach enhances diagnostic efficiency and accuracy in hematological disorders.