https://phaidra.vetmeduni.ac.at/o:3991 Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies Wulcan, Judit M. Judit M. Wulcan 0000-0002-0624-9611 Giaretta, Paula R. Paula R. Giaretta 0000-0003-2261-281X Fingerhood, Sai Sai Fingerhood 0000-0003-1588-0672 de Brot, Simone Simone de Brot 0000-0003-3049-0103 Crouch, Esther E. V. Esther E. V. Crouch Wolf, Tatiana Tatiana Wolf Isabel Casanova, Maria Maria Isabel Casanova Ruivo, Pedro R. Pedro R. Ruivo Bolfa, Pompei Pompei Bolfa 0000-0002-2903-1535 Streitenberger, Nicolás Nicolás Streitenberger Bertram, Christof A. Christof A. Bertram 0000-0002-2402-9997 University of Veterinary Medicine Vienna Donovan, Taryn A Taryn A Donovan 0000-0001-5740-9550 Keel, Michael Kevin Michael Kevin Keel 0000-0002-0995-1617 Moore, Peter F. Peter F. Moore Keller, Stefan M. Stefan M. Keller 0000-0002-5428-2985 Sage journal article open access eng Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological 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-stained small intestinal biopsies from cats. The median sensitivity, positive predictive value, and F1 score of the AI model compared with 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. Errors included false negatives in whole-slide images with faded stain and false positives in 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, the AI-derived lymphocyte counts overlapped 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 feline chronic enteropathy. Animals Cats Intestine, Small Pathology Intestine, Small Cytology Artificial Intelligence Cat Diseases Pathology Cat Diseases Diagnosis Biopsy Veterinary Lymphocytes Pathology Reproducibility of Results Observer Variation Lymphocyte Count Veterinary Male Female Sensitivity and Specificity http://creativecommons.org/licenses/by-nc/4.0/ https://phaidra.vetmeduni.ac.at/api/object/o:3991/download 10.1177/03009858241286828 https://phaidra.vetmeduni.ac.at/o:605 application/pdf Veterinary Pathology issn:1544-2217 VoR Veterinary Pathology 62 2 2.92 MB 2024