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

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Published on: April 8, 2015

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

Xinyan Fu¹, May Fu², Qiang Li¹

¹Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, China.

Acta Cytologica

|July 30, 2020

Summary

This summary is machine-generated.

An AI-powered system, Morphogo, automates bone marrow cell differential counts, showing high accuracy and potential for clinical use in diagnosing hematological malignancies.

Keywords:

Automatic cell classification Bone marrow aspirate smear Cell morphology Digital image

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Area of Science:

Hematology
Medical Diagnostics
Artificial Intelligence in Medicine

Background:

Manual bone marrow differential counts are crucial for diagnosing hematological malignancies but are time-consuming and inconsistent.
A novel artificial intelligence (AI)-based system was developed to automate bone marrow cell classification.

Purpose of the Study:

To develop and evaluate an AI-based system for automatic classification of bone marrow cells.
To assess the clinical applicability of the automated system for differential counts.

Main Methods:

Bone marrow aspirate smears were digitized using the Morphogo automated analysis system.
An AI platform analyzed nucleated marrow cells at ×1,000 magnification.
System performance was evaluated for accuracy, sensitivity, and specificity, with results compared to manual counts by pathologists.

Main Results:

The AI system achieved over 85.7% classification accuracy for hematopoietic lineage cells.
Average sensitivity and specificity were 69.4% and 97.2%, respectively.
Automated differential cell percentages correlated well with manual counts for granulocytes, erythrocytes, and lymphocytes (r ≥ 0.762).

Conclusions:

The Morphogo system demonstrates reliability for automated bone marrow cell differential count analysis.
The AI system shows potential for clinical applications in hematological diagnostics.
Further large-scale multicenter validation studies are underway to confirm clinical utility.