Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.
Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to per...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 10 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146224568&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146224568 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2020 vid: 44 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146224568 146224568 146224568 10.1007/s10916-020-01654-y 146224568 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study. aug: au: Jin, Hong Fu, Xinyan Cao, Xinyi Sun, Mingxia Wang, Xiaofen Zhong, Yuhong Yang, Suwen Qi, Chao Peng, Bo He, Xin He, Fei Jiang, Yongfang Gao, Haiyan Li, Shun Huang, Zhen Li, Qiang Fang, Fengqi Zhang, Jun affil: Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 310016, Hangzhou, Zhejiang, China sug: subj: Bone Marrow Examination Hematologic Diseases Diagnosis Machine Learning Cell Count Neural Networks (Computer) Human Pilot Studies Retrospective Design China Descriptive Statistics Confidence Intervals Classification Algorithms Software Workflow ab: Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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