Standardizing canine breed data in veterinary records is challenging, but computer vision offers an alternative perspective on breed assignment.

Abstract

Dog breed is fundamental health information, especially in the context of breed-linked diseases. The standardization of breed terminology across health records is necessary to leverage the big data revolution for veterinary research. Breed can also inform clinical decision making. However, client-reported breeds vary in their reliability depending on how breed was determined. Surprisingly, research in computer science reports that AI can assign breed to dogs with over 90% accuracy from a photograph. Here, we explore the extent to which current research in AI is relevant to breed assignment or validation in veterinary contexts. This review provides a primer on approaches used in dog breed identification and the datasets used to train models to identify breed. Closely examining these datasets reveals that AI research uses unreliable definitions of breed and therefore does not currently generate predictions relevant in veterinary contexts. We identify issues with the curation of the datasets used to develop these models, which are also likely to depress model performance as evaluated within the field of AI. Therefore, expert curation of datasets that can be used alongside existing algorithms is likely to improve research on this topic in both fields. Such advances will only be possible through collaboration between veterinary experts and computer scientists.