Evaluation of machine learning-driven automated Kleihauer-Betke counting: A method comparison study.

Journal: International journal of laboratory hematology
PMID: 33151041

Abstract

INTRODUCTION: The Kleihauer-Betke (KB) test is the diagnostic standard for the quantification of fetomaternal hemorrhage (FMH). Manual analysis of KB slides suffers from inter-observer and inter-laboratory variability and low efficiency. Flow cytometry provides accurate quantification of FMH with high efficiency but is not available in all hospitals or at all times. We have developed an automated KB counting system that uses machine learning to identify and distinguish fetal and maternal red blood cells (RBCs). In this study, we aimed to evaluate and compare the accuracy, precision, and efficiency of the automated KB counting system with manual KB counting and flow cytometry.

Authors

  • Zhuoran Zhang
  • Ji Ge
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
  • Zheng Gong
    Sino-Cellbiomed Institutes of Medical Cell & Pharmaceutical Proteins Qingdao University, Qingdao, Shandong, China. xblong2000@gmail.com.
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yu Sun
    Department of Neurology, China-Japan Friendship Hospital, Beijing, China.

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