Author ORCID Identifier
Glenvelis Perez: 0009-0000-6092-1547
Yixuan He: 0009-0007-5692-8608
Zihan Lyu: 0009-0002-5325-6838
Yilin Chen: 0009-0009-2640-8235
Nicholas R. Howe: 0000-0002-4427-9985
Halie M. Rando: 0000-0001-7688-1770
Document Type
Article
Publication Date
3-1-2025
Publication Title
American Journal of Veterinary Research
Abstract
Dog breed is fundamental health information, especially in the context of breed-linked diseases. The standard-ization 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.
Keywords
breed identification, breed phylogenetics, computer vision, dog breeds, veterinary health AI
Volume
86
Issue
S1
First Page
38
Last Page
45
DOI
10.2460/ajvr.24.10.0315
ISSN
00029645
Recommended Citation
Perez, Glenvelis; He, Yixuan; Lyu, Zihan; Chen, Yilin; Howe, Nicholas; and Rando, Halie M., "Standardizing Canine Breed Data in Veterinary Records Is Challenging, but Computer Vision Offers an Alternative Perspective on Breed Assignment" (2025). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/408
Comments
Archived as published.