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Related Concept Videos

Classification of Leukocytes01:30

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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A Digital Tool Supporting Pathology Practice and Identifying Leucocytes.

Dumitru-Cristian Apostol1, Antonela-Maria Chiuzbăian2, Mihaela Crisan-Vida1

  • 1Faculty of Automation and Computers, University Politehnica Timişoara, Romania.

Studies in Health Technology and Informatics
|August 23, 2024
PubMed
Summary

This study introduces an automated digital pathology tool for precise leucocyte counting and mass determination from images. The system achieves high accuracy, aiding pathologists in disease outcome prediction with minimal manual input.

Keywords:
Artificial IntelligenceIntelligent ModelsPathology

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Area of Science:

  • Digital Pathology
  • Computational Biology
  • Medical Image Analysis

Background:

  • Pathology workflows often require manual cell counting and analysis, which is time-consuming and prone to inter-observer variability.
  • Accurate quantification of leucocytes and their characteristics is crucial for diagnosing and predicting disease outcomes.
  • Existing digital pathology tools may lack comprehensive traceability or require significant pathologist intervention.

Purpose of the Study:

  • To develop an integrated digital pathology solution for automated leucocyte analysis.
  • To provide accurate quantification of leucocyte count and cell mass directly from pathology images.
  • To establish traceability between identified leucocytes and potential disease outcomes, facilitating a Proof of Concept (PoC) or prototype.

Main Methods:

  • Development of an all-in-one package integrating data manipulation tools, predefined analytical models, and a digital pathology interface.
  • Training machine learning models on a dataset of approximately 20,000 images for leucocyte identification and quantification.
  • Implementation of visual scripting and an intuitive interface to reduce the learning curve for pathology analysis.

Main Results:

  • The developed model achieved an overall accuracy of approximately 85% in leucocyte analysis.
  • The system demonstrated a true positive prediction rate of approximately 82% for identified areas of interest.
  • The models correctly identified approximately 89% of positive instances flagged by pathologists, with a 6% false positive rate on negative instances.

Conclusions:

  • The automated digital pathology tool effectively quantifies leucocytes and their mass from images with minimal pathologist intervention.
  • The system offers high accuracy in identifying and classifying leucocyte areas, supporting diagnostic and prognostic assessments.
  • The integrated platform enhances pathology analysis efficiency and provides valuable traceability for clinical decision-making.