Use of Machine Learning--Based Software for the Screening of Thyroid Cytopathology Whole Slide Images.
Context.--The use of whole slide images (WSIs) in diagnostic pathology presents special challenges for the cytopathologist. Informative areas on a direct smear from a thyroid fine-needle aspiration biopsy (FNAB) smear may be spread across a large area comprising blood and dead space. Manually naviga...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 7; pp. 872 - 879 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Jul2022
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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=157742366&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157742366 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Jul2022 vid: 146 iid: 7 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 157742366 157742366 157742366 10.5858/arpa.2020-0712-OA 157742366 ppf: 872 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Use of Machine Learning--Based Software for the Screening of Thyroid Cytopathology Whole Slide Images. aug: au: Dov, David Kovalsky, Shahar Z. Qizhang Feng Assaad, Serge Cohen, Jonathan Bell, Jonathan Henao, Ricardo Carin, Lawrence Elliott Range, Danielle affil: Departments of Electrical and Computer Engineering, Duke University, Durham, North Carolina sug: subj: Machine Learning Thyroid Gland Pathology Diagnostic Imaging Statistics and Numerical Data Software Health Screening Diagnosis, Computer Assisted Pathology, Clinical Biopsy, Needle Aspiration Algorithms Reference Books Image Processing, Computer Assisted Cytological Techniques, Automated Data Analysis Software ab: Context.--The use of whole slide images (WSIs) in diagnostic pathology presents special challenges for the cytopathologist. Informative areas on a direct smear from a thyroid fine-needle aspiration biopsy (FNAB) smear may be spread across a large area comprising blood and dead space. Manually navigating through these areas makes screening and evaluation of FNA smears on a digital platform time-consuming and laborious. We designed a machine learning algorithm that can identify regions of interest (ROIs) on thyroid fine-needle aspiration biopsy WSIs. Objective.--To evaluate the ability of the machine learning algorithm and screening software to identify and screen for a subset of informative ROIs on a thyroid FNA WSI that can be used for final diagnosis. Design.--A representative slide from each of 109 consecutive thyroid fine-needle aspiration biopsies was scanned. A cytopathologist reviewed each WSI and recorded a diagnosis. The machine learning algorithm screened and selected a subset of 100 ROIs from each WSI to present as an image gallery to the same cytopathologist after a washout period of 117 days. Results.--Concordance between the diagnoses using WSIs and those using the machine learning algorithm-- generated ROI image gallery was evaluated using pairwise weighted j statistics. Almost perfect concordance was seen between the 2 methods with a j score of 0.924. Conclusions.--Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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