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Related Experiment Video

Updated: Jun 22, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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Recent advancements in machine learning for bone marrow cell morphology analysis.

Yifei Lin1,2, Qingquan Chen1,3, Tebin Chen1

  • 1The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.

Frontiers in Medicine
|July 1, 2024
PubMed
Summary

Machine learning, particularly deep learning, offers powerful tools for analyzing bone marrow cell morphology, aiding in early disease detection. This review guides hematologists in selecting AI algorithms for automated analysis, improving diagnostic accuracy and efficiency.

Keywords:
artificial intelligenceautomatic classificationautomatic identificationbone marrow cell morphologydeep learningmachine learningvisualization

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Hematology

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly used in medicine for analyzing large datasets.
  • Deep learning excels in image processing, offering robust feature learning for medical applications.
  • Manual bone marrow cell morphology analysis, while standard, has limitations necessitating automated solutions.

Purpose of the Study:

  • To review current research on machine learning applications in bone marrow cell morphology analysis.
  • To provide recommendations for hematologists on selecting appropriate ML algorithms for automation.
  • To identify future research directions in AI-driven bone marrow analysis.

Main Methods:

  • The review synthesizes current research on six key automated bone marrow cell morphology processes.
  • It highlights advancements in machine learning systems applied to bone marrow cell morphology.
  • The study focuses on deep neural networks and other ML paradigms for image analysis.

Main Results:

  • AI and ML demonstrate significant potential to augment clinical diagnostics in bone marrow analysis.
  • Automated methods offer improvements in cell detection, segmentation, identification, classification, enumeration, and diagnosis.
  • Machine learning systems show promise for swift and precise analysis of cytopathic trends.

Conclusions:

  • Machine learning, especially deep learning, is a valuable tool for automating bone marrow cell morphology examinations.
  • Selecting the right ML algorithm is crucial for efficient and accurate hematologic disorder diagnosis.
  • Further research is needed to fully realize the potential of AI in bone marrow cytopathology.