Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells
Fast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a...
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.749146/full |
| _version_ | 1798025092495572992 |
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| author | Luyue Jiang Gang Niu Yangyang Liu Wenjin Yu Heping Wu Zhen Xie Matthew Xinhu Ren Yi Quan Yi Quan Zhuangde Jiang Gang Zhao Wei Ren |
| author_facet | Luyue Jiang Gang Niu Yangyang Liu Wenjin Yu Heping Wu Zhen Xie Matthew Xinhu Ren Yi Quan Yi Quan Zhuangde Jiang Gang Zhao Wei Ren |
| author_sort | Luyue Jiang |
| collection | DOAJ |
| description | Fast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a deep neural network (DNN) for cell counting and classification of cerebrospinal fluid cytology. The May-Grünwald-Giemsa (MGG) stained image is annotated and input into the DNN network. The main cell types include lymphocytes, monocytes, neutrophils, and red blood cells. In clinical practice, the use of DNN is compared with the results of expert examinations in the professional cerebrospinal fluid room of a First-line 3A Hospital. The results show that the report produced by the DNN network is more accurate, with an accuracy of 95% and a reduction in turnaround time by 86%. This study shows the feasibility of applying DNN to clinical cerebrospinal fluid cytology. |
| first_indexed | 2024-04-11T18:12:58Z |
| format | Article |
| id | doaj.art-7345ec827d654a04918d0abb1014dc7d |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-11T18:12:58Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj.art-7345ec827d654a04918d0abb1014dc7d2022-12-22T04:10:03ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-01-01810.3389/fmed.2021.749146749146Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid CellsLuyue Jiang0Gang Niu1Yangyang Liu2Wenjin Yu3Heping Wu4Zhen Xie5Matthew Xinhu Ren6Yi Quan7Yi Quan8Zhuangde Jiang9Gang Zhao10Wei Ren11Electronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaElectronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaElectronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaThe College of Life Sciences and Medicine, Northwest University, Xi'an, ChinaElectronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaThe College of Life Sciences and Medicine, Northwest University, Xi'an, ChinaBiology Program, Faculty of Science, The University of British Columbia, Vancouver, BC, CanadaElectronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaSchool of Microelectronics, Xidian University, Xi'an, ChinaThe State Key Laboratory for Manufacturing Systems Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaThe College of Life Sciences and Medicine, Northwest University, Xi'an, ChinaElectronic Materials Research Laboratory, School of Electronic Science and Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, ChinaFast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a deep neural network (DNN) for cell counting and classification of cerebrospinal fluid cytology. The May-Grünwald-Giemsa (MGG) stained image is annotated and input into the DNN network. The main cell types include lymphocytes, monocytes, neutrophils, and red blood cells. In clinical practice, the use of DNN is compared with the results of expert examinations in the professional cerebrospinal fluid room of a First-line 3A Hospital. The results show that the report produced by the DNN network is more accurate, with an accuracy of 95% and a reduction in turnaround time by 86%. This study shows the feasibility of applying DNN to clinical cerebrospinal fluid cytology.https://www.frontiersin.org/articles/10.3389/fmed.2021.749146/fullneural networkwhite blood cellcerebral spinal fluidclassificationclinicalimage recognition |
| spellingShingle | Luyue Jiang Gang Niu Yangyang Liu Wenjin Yu Heping Wu Zhen Xie Matthew Xinhu Ren Yi Quan Yi Quan Zhuangde Jiang Gang Zhao Wei Ren Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells Frontiers in Medicine neural network white blood cell cerebral spinal fluid classification clinical image recognition |
| title | Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells |
| title_full | Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells |
| title_fullStr | Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells |
| title_full_unstemmed | Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells |
| title_short | Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells |
| title_sort | establishment and verification of neural network for rapid and accurate cytological examination of four types of cerebrospinal fluid cells |
| topic | neural network white blood cell cerebral spinal fluid classification clinical image recognition |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.749146/full |
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