Experimental evaluation of deep learning method in reticulocyte enumeration in peripheral blood. (20th May 2021)

• Record Type: Journal Article
• Authors: Wang, Geng; Zhao, Tianci; Fang, Zhejun; Lian, Heqing; Wang, Xin; Li, Zepeng; Wu, Wei; Li, Bairui; Zhang, Qian
• Is Part Of: International journal of laboratory hematology. Volume 43:Number 4(2021)
• Page Start: 597
• Page End: 601
• Publication Date: 2021-05-20
• Subjects: deep learning -- morphology -- red blood cell -- reticulocyte -- reticulocyte enumeration; Hematology -- Periodicals; Blood -- Diseases -- Periodicals; Hematology -- Periodicals; 616.15005
• DOI: 10.1111/ijlh.13588
• Languages: English
• ISSNs: 1751-5521

Abstract

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. Methods: 40 EDTA‐K2 anticoagulated whole blood samples were randomly selected for the study. 784 microscopic images were taken from blood slides as dataset, and all mature RBCs and RETs in these images were located and labeled by experienced experts. Then, we leverage a Faster R‐CNN deep neural network to train a RET detection model and evaluate the model. Results: Both the recall and precision rate of the model are more than 97%, and average analysis time of a single image is 0.21 seconds. Conclusion: The deep learning method shows outstanding performance including high accuracy and fast speed. The experimental results show that the deep learning method holds the potential to act as a rapid computer‐aid method for manual RET enumeration for cytological examiners.