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A method to classify bone marrow cells with rejected option.

Liang Guo1,2, Peiduo Huang1, Haisen He1

  • 1Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.

Biomedizinische Technik. Biomedical Engineering
|April 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for classifying bone marrow cells, improving diagnostic accuracy. The CMWRO system achieves high precision and accuracy while identifying uncertain cases for expert review, enhancing efficiency.

Keywords:
CNN-ICP-SoftMaxbonemarrow cell classificationclassifier with rejected optionconformal predictordeep learning

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Area of Science:

  • Hematology
  • Computational Biology
  • Medical Imaging

Background:

  • Bone marrow cell morphology is crucial for diagnosing blood diseases but relies on subjective, experience-dependent interpretation.
  • Current automated systems lack reliability by not handling low-confidence predictions, posing risks in critical applications like bone marrow analysis.

Purpose of the Study:

  • To develop an automated bone marrow cell classification system that incorporates a mechanism to reject uncertain predictions.
  • To enhance the reliability and efficiency of bone marrow cell recognition by integrating AI with expert review.

Main Methods:

  • A novel bone marrow cell classification method with rejected option (CMWRO) was developed, utilizing a convolutional neural network, ICP, and SoftMax (CNN-ICP-SoftMax).
  • The system classifies 11 types of bone marrow cells and is designed to identify and reject samples with low prediction confidence.

Main Results:

  • At a rejected rate of 0.3143, the CMWRO system achieved high performance metrics for accepted samples: precision (0.9921), sensitivity (0.9917), and accuracy (0.9944).
  • The method demonstrated over 82% filtering efficiency for untrained or rare cell types, including abnormal cells.
  • Rejected samples are flagged for manual review by physicians, combining AI and expert judgment.

Conclusions:

  • The CMWRO system significantly improves the efficiency and accuracy of bone marrow cell recognition.
  • By selectively rejecting uncertain cases, the system increases physician confidence in the automated results and streamlines the diagnostic workflow.