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Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation

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Published on: November 3, 2018

Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.

Gongchen Wang^1,2, Guangyu Xu^1,2, Yao An^1,2

¹Department of Medical Laboratory Science and Technology, Harbin Medical University-Daqing, Daqing 163000, China.

Diagnostics (Basel, Switzerland)

|April 14, 2026

Summary

A new deep learning tool accurately detects abnormal promyelocytes in peripheral blood smears, aiding early acute promyelocytic leukemia (APL) screening. This rapid, image-based method shows promise for improving APL diagnosis and patient outcomes.

Keywords:

acute promyelocytic leukemia convolutional neural network deep learning peripheral blood smear

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Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

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Published on: February 21, 2021

Area of Science:

Hematology
Medical Imaging
Artificial Intelligence

Background:

Acute promyelocytic leukemia (APL) is a dangerous subtype of acute myeloid leukemia.
Current diagnostic methods are invasive, time-consuming, and costly.
There is a need for rapid, non-invasive APL screening tools.

Purpose of the Study:

To develop a deep learning (DL) model for automated detection of abnormal promyelocytes in peripheral blood smears (PBSs).
To create a rapid, explainable, and accurate auxiliary tool for early APL suspicion.
To improve patient compliance by avoiding invasive bone marrow aspiration.

Main Methods:

A multi-stage DL model (EfficientDet) was developed to read PBS images and identify abnormal promyelocytes.
The model was trained on 223,123 cell images from bone marrow and PBS samples.
Performance was evaluated on 150 PBSs and compared to manual microscopy by pathologists.

Main Results:

The DL model demonstrated superior screening performance in identifying abnormal promyelocytes compared to pathologists.
EfficientDet accurately segmented cells and detected abnormal promyelocytes using only image data.
The model achieved high accuracy in APL screening.

Conclusions:

The developed DL approach is a promising tool for detecting abnormal promyelocytes.
This method can facilitate early APL screening and raise awareness for suspected cases.
The tool has the potential to reduce diagnostic delays and improve patient care.