Experimental evaluation of deep learning method in reticulocyte enumeration in peripheral blood
| Autor: | Xin Wang, Geng Wang, Li Bairui, Fang Zhejun, Lian Heqing, Wei Wu, Qian Zhang, Zhao Tianci, Zepeng Li |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Reticulocytes Computer science Clinical Biochemistry 030204 cardiovascular system & hematology 03 medical and health sciences Deep Learning 0302 clinical medicine Reticulocyte Count Reticulocyte medicine Enumeration Humans Leverage (statistics) Single image Artificial neural network business.industry Deep learning Biochemistry (medical) Pattern recognition Hematology General Medicine Flow Cytometry Peripheral blood medicine.anatomical_structure Female Artificial intelligence Precision and recall business 030215 immunology |
| Zdroj: | International Journal of Laboratory Hematology. 43:597-601 |
| ISSN: | 1751-553X 1751-5521 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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