biostudies-literatureWulcan JMCenter for Companion Animal Health (CCAH), UC Davis, School of Veterinary MedicineUC Davis Charles River Laboratories Digital Pathology FellowshipUC Davis Comprehensive Cancer InstituteNCI NIH HHSPeter C. Kennedy Endowed Fellowship for Veterinary Anatomic Pathology139-151https://www.ebi.ac.uk/biostudies/studies/S-EPMC11874495biostudies-literatureUnknown62(2)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% (iVeterinary pathologyArtificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.PMC11874495#2020-3-FNCI P30CA093373P30 CA093373Keller SMWolf TWulcan JMGiaretta PRFingerhood SBolfa PStreitenberger NKeel MKde Brot SCrouch EEVRuivo PRMoore PFBertram CAIsabel Casanova MDonovan TAfalseArtificial 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% (i2025-01-01T00:00:00Z2025 Mar2026-05-29T14:47:58.059Z2025-04-19T13:40:33.077ZS-EPMC118744953940005110.1177/03009858241286828