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Chen X, Huang X, Zhang J, Wang M, Chen D, Li Y, Qin X, Wang J, Chen J. Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks. Cytometry A 2024; 105:315-322. [PMID: 38115230 DOI: 10.1002/cyto.a.24823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/21/2023]
Abstract
The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.
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Affiliation(s)
- Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xukun Huang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Zhang
- Beijing Institute of Genomics, China National Centre for Bioinformation, Chinese Academy of Sciences, Beijing, China
| | - Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yueying Li
- Beijing Institute of Genomics, China National Centre for Bioinformation, Chinese Academy of Sciences, Beijing, China
| | - Xuzhen Qin
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
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