phaidra.vetmeduni.ac.at o:3991 DOI 10.1177/03009858241286828 Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies 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. eng 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 2025-03-31T09:14:05.345Z LOMv1.0 Author BEGIN:VCARD VERSION:3.0 N:Wulcan;Judit M.; FN:Judit M. Wulcan X-ORCID:https://orcid.org/0000-0002-0624-9611 END:VCARD BEGIN:VCARD VERSION:3.0 N:Giaretta;Paula R.; FN:Paula R. Giaretta X-ORCID:https://orcid.org/0000-0003-2261-281X END:VCARD BEGIN:VCARD VERSION:3.0 N:Fingerhood;Sai; FN:Sai Fingerhood X-ORCID:https://orcid.org/0000-0003-1588-0672 END:VCARD BEGIN:VCARD VERSION:3.0 N:de Brot;Simone; FN:Simone de Brot X-ORCID:https://orcid.org/0000-0003-3049-0103 END:VCARD BEGIN:VCARD VERSION:3.0 N:Crouch;Esther E. V.; FN:Esther E. V. Crouch END:VCARD BEGIN:VCARD VERSION:3.0 N:Wolf;Tatiana; FN:Tatiana Wolf END:VCARD BEGIN:VCARD VERSION:3.0 N:Isabel Casanova;Maria; FN:Maria Isabel Casanova END:VCARD BEGIN:VCARD VERSION:3.0 N:Ruivo;Pedro R.; FN:Pedro R. Ruivo END:VCARD BEGIN:VCARD VERSION:3.0 N:Bolfa;Pompei; FN:Pompei Bolfa X-ORCID:https://orcid.org/0000-0002-2903-1535 END:VCARD BEGIN:VCARD VERSION:3.0 N:Streitenberger;Nicolás; FN:Nicolás Streitenberger END:VCARD BEGIN:VCARD VERSION:3.0 N:Bertram;Christof A.; FN:Christof A. Bertram X-ORCID:https://orcid.org/0000-0002-2402-9997 END:VCARD BEGIN:VCARD VERSION:3.0 N:Donovan;Taryn A; FN:Taryn A Donovan X-ORCID:https://orcid.org/0000-0001-5740-9550 END:VCARD BEGIN:VCARD VERSION:3.0 N:Keel;Michael Kevin; FN:Michael Kevin Keel X-ORCID:https://orcid.org/0000-0002-0995-1617 END:VCARD BEGIN:VCARD VERSION:3.0 N:Moore;Peter F.; FN:Peter F. Moore END:VCARD BEGIN:VCARD VERSION:3.0 N:Keller;Stefan M.; FN:Stefan M. Keller X-ORCID:https://orcid.org/0000-0002-5428-2985 END:VCARD https://w3id.org/kim/hcrt/scheme https://w3id.org/kim/hcrt/text Textdokument Text LOMv1.0 yes https://creativecommons.org/licenses/by-nc/4.0 application/pdf 3062695 https://phaidra.vetmeduni.ac.at/o:3991 https://phaidra.vetmeduni.ac.at/api/object/o:3991/thumbnail LOMv1.0 discipline