Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies

Feline chronic enteropathy (FCE) is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histopathological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for classification and grading. However, conventional histological methods for lymphocyte quantification have low interobserver agreement, resulting in low diagnostic reliability. This study aimed to develop and validate an Artificial Intelligence (AI) model to detect intraepithelial and lamina propria lymphocytes in hematoxylin and eosin (H&E) stained small intestinal biopsies from cats to assist pathologists in quantifying lymphocytes in FCE biopsies. Median sensitivity, positive predictive value and F1 score per validation region, of the AI-model compared to the majority opinion of 11 veterinary anatomic pathologists, were 100% (interquartile range (IQR) 67-100%), 57% (IQR 38-83%) and 67% (IQR 43-80%) for intraepithelial lymphocytes, and 89% (IQR 71-100%), 67% (IQR 50-82%) and 70% (IQR 43-80%) for lamina propria lymphocytes, respectively. Notable errors included false negatives in whole slide images with faded stain and false positives misidentifying enterocyte nuclei. Semiquantitative grading at the whole slide level showed low interobserver agreement among pathologists, underscoring the need for a reproducible quantitative approach. While semiquantitative grade and AI-derived lymphocyte counts correlated positively, there was a notable lymphocyte count overlap between different grades. Our AI-model, when supervised by a pathologist, offers a reproducible, objective, and quantitative assessment of feline intestinal lymphocytes at the whole slide level, and has the potential to enhance diagnostic accuracy and consistency for FCE.