Experimental evaluation of deep learning method in reticulocyte enumeration in peripheral blood.

Journal: International journal of laboratory hematology
PMID: 34014615

Abstract

INTRODUCTION: Reticulocytes (RET) are immature red blood cells, and RET enumeration in peripheral blood has important clinical value in diagnosis, treatment efficacy observation, and prognosis of anemic diseases. For RET enumeration, flow cytometric reference method has shown to be more precise than the manual method by light microscopy. However, flow cytometric method generates occasionally spurious RET counts in some situations. The manual method, which is subjective, imprecise, and tedious, currently remains as an accepted reference method. As a result, there is a need for manual method to be more objective, precise, and rapid.

Authors

  • Geng Wang
    Beijing Jishuitan Hospital, Beijing, China.
  • Tianci Zhao
    Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China.
  • Zhejun Fang
    Beijing Xiaoying Technology Co., Ltd, Beijing, China.
  • Heqing Lian
    Beijing Xiaoying Technology Co., Ltd, Beijing, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Zepeng Li
    Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Bairui Li
    Beijing Xiaoying Technology Co., Ltd, Beijing, China.
  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.

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