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Classification of Leukocytes

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Related Experiment Video

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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

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm.

Yuping Yang^1,2, Hong He¹, Junju Wang¹

¹Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China. shunbo.li@cqu.edu.cn.

|March 22, 2023

Summary

This summary is machine-generated.

A new low-cost method uses microfluidics and deep learning to classify red blood cells (RBCs) by shape. This approach accurately assesses stored blood quality, improving transfusion outcomes.

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Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

Area of Science:

Biomedical Engineering
Cell Biology
Machine Learning

Background:

Red blood cell (RBC) quality is crucial for transfusion efficacy.
Traditional blood quality assessment is reagent-intensive and time-consuming.

Purpose of the Study:

To develop a rapid, low-cost, label-free method for RBC morphology classification.
To enable precise evaluation of stored blood quality using AI and microfluidics.

Main Methods:

Integrated microfluidic technology with a deep learning object detection model.
Designed microfluidic channels to prevent cell overlap for clear imaging.
Optimized deep learning model for simultaneous recognition and classification of RBCs into six morphological subtypes.

Main Results:

Achieved 89.24% mean average precision in RBC morphological classification.
Developed a morphology index (MI) to quantify blood quality.
Demonstrated accurate blood quality assessment for samples stored up to 42 days (MIs: 84.53%, 73.33%, 24.34%).

Conclusions:

The combined microfluidic and deep learning approach offers a high-precision, efficient method for RBC quality evaluation.
This technique is versatile and applicable to general cell identification based on morphology.
Potential to significantly improve blood banking and transfusion medicine practices.