Evidence map›Paper›PMID 35141238›Full record

ArticleFrontiers in medicine2021

Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells.

Luyue Jiang, Gang Niu, Yangyang Liu, Wenjin Yu, Heping Wu, Zhen Xie, Matthew Xinhu Ren, Yi Quan, Zhuangde Jiang, Gang Zhao and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 49% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Luyue JiangElectronic 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, China.
Gang NiuElectronic 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, China.
Yangyang LiuElectronic 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, China.
Wenjin YuThe College of Life Sciences and Medicine, Northwest University, Xi'an, China.
Heping WuElectronic 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, China.
Zhen XieThe College of Life Sciences and Medicine, Northwest University, Xi'an, China.
Matthew Xinhu RenBiology Program, Faculty of Science, The University of British Columbia, Vancouver, BC, Canada.
Yi QuanElectronic 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, China.
Zhuangde JiangThe State Key Laboratory for Manufacturing Systems Engineering, The International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology, Xi'an Jiaotong University, Xi'an, China.
Gang ZhaoThe College of Life Sciences and Medicine, Northwest University, Xi'an, China.
Wei RenElectronic 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, China.
Xi'an Jiaotong University · CNNorthwest University · CNUniversity of British Columbia · CAXidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

cerebral spinal fluidclassificationclinicalimage recognitionneural networkwhite blood cell

Identifiers

PMID35141238
PMCPMC8818991
OpenAlexW4206948011

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.