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    Use of Machine Learning-Based Software for the Screening of Thyroid Cytopathology Whole Slide Images.

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    10.5858_arpa.2020-0712-oa.pdf
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    1.211Mb
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Dov, David
    Kovalsky, Shahar Z
    Feng, Qizhang
    Assaad, Serge
    Cohen, Jonathan
    Bell, Jonathan
    Henao, Ricardo
    Carin, Lawrence cc
    Range, Danielle Elliott
    KAUST Department
    Office of the VP
    Academic Affairs
    Date
    2021-10-20
    Online Publication Date
    2021-10-20
    Print Publication Date
    2022-07-01
    Permanent link to this record
    http://hdl.handle.net/10754/672961
    
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    Abstract
    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. 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. 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. Concordance between the diagnoses using WSIs and those using the machine learning algorithm-generated ROI image gallery was evaluated using pairwise weighted κ statistics. Almost perfect concordance was seen between the 2 methods with a κ score of 0.924. Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads.
    Citation
    Dov, D., Kovalsky, S. Z., Feng, Q., Assaad, S., Cohen, J., Bell, J., … Range, D. E. (2021). Use of Machine Learning–Based Software for the Screening of Thyroid Cytopathology Whole Slide Images. Archives of Pathology & Laboratory Medicine. doi:10.5858/arpa.2020-0712-oa
    Journal
    Archives of pathology & laboratory medicine
    DOI
    10.5858/arpa.2020-0712-oa
    PubMed ID
    34669924
    Additional Links
    https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2020-0712-OA/472362/Use-of-Machine-Learning-Based-Software-for-the
    ae974a485f413a2113503eed53cd6c53
    10.5858/arpa.2020-0712-oa
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