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.
The authors have no relevant financial interest in the products or companies described in this article.