| Summary: | In dogs, the <i>BRAF</i> mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect <i>BRAF</i> mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict <i>BRAF</i> mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (<i>n</i> = 81) and a validation set (<i>n</i> = 96). Among 96 WSI, 57 showed identical PCR and AI-based <i>BRAF</i> predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (<i>p</i> < 0.01), presence of inflammation (<i>p</i> < 0.01), slide quality (<i>p</i> < 0.02) and sample size (<i>p</i> < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict <i>BRAF</i> mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections.
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