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Use of Machine Learning-Based Software for the Screening of Thyroid Cytopathology Whole Slide Images.
- Source :
-
Archives of pathology & laboratory medicine [Arch Pathol Lab Med] 2022 Jul 01; Vol. 146 (7), pp. 872-878. - Publication Year :
- 2022
-
Abstract
- 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.<br />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.<br />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.<br />Results.—: 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.<br />Conclusions.—: Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads.
Details
- Language :
- English
- ISSN :
- 1543-2165
- Volume :
- 146
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Archives of pathology & laboratory medicine
- Publication Type :
- Academic Journal
- Accession number :
- 34669924
- Full Text :
- https://doi.org/10.5858/arpa.2020-0712-OA