Abstract
center dot 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. (Arch Pathol Lab Med. 2022;146:872-878; doi: 10.5858/ arpa.2020-0712-OA)