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

Comments

Archived as published.

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