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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

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Published on: June 4, 2015

Digital Imaging and AI Pre-classification in Hematology.

Kelly A Bowers¹, Megan O Nakashima¹

¹Department of Pathology and Laboratory Medicine, Cleveland Clinic, 9500 Euclid Avenue L30, Cleveland, OH 44195, USA.

Clinics in Laboratory Medicine

|August 1, 2024

Summary

Digital imaging with artificial neural networks offers comparable performance to manual microscopy for blood cell analysis. These systems enhance standardization, efficiency, and remote review capabilities for trained morphologists.

Keywords:

Differential count Digital imaging Neural networks Pre-classification

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Area of Science:

Hematology
Digital Pathology
Artificial Intelligence in Medicine

Background:

Peripheral blood leukocyte differential counts are crucial for diagnosing various medical conditions.
Traditional manual optical microscopy is time-consuming and subject to inter-observer variability.
Advancements in digital imaging and artificial intelligence (AI) offer potential improvements in blood cell analysis.

Purpose of the Study:

To evaluate the utility of digital imaging systems with AI for leukocyte differential counts.
To assess the capabilities of these systems in evaluating platelet and erythrocyte morphology and estimating counts.
To highlight the benefits of AI-driven systems in clinical hematology practice.

Main Methods:

Utilizing digital imaging systems for peripheral blood smear analysis.
Employing artificial neural networks for cellular pre-classification.
Assessing platelet and erythrocyte morphology and estimating cell counts using the digital systems.

Main Results:

Digital imaging systems with AI demonstrate comparable performance to manual optical microscopy for leukocyte differentials.
These systems can effectively assess platelet and erythrocyte morphology and estimate cell counts.
Systems from a single vendor are in clinical use, with others under development.

Conclusions:

AI-powered digital imaging systems provide a viable alternative to manual microscopy for blood cell analysis.
Key benefits include increased standardization, improved efficiency, and remote-review capabilities.
These systems are intended for use by trained morphologists to augment diagnostic processes.