Review of applications of deep learning in veterinary diagnostics and animal health

作者：Sam Xiao, Navneet K. Dhand, Zhiyong Wang, Kun Hu, Peter C. Thomson, John K. House, Mehar S. Khatkar · 发表于：Frontiers in Veterinary Science · 年份：2025 · DOI：10.3389/fvets.2025.1511522 · 被引用次数：53 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Artificial Intelligence in Healthcare and Education

Deep learning (DL), a subfield of artificial intelligence (AI), involves the development of algorithms and models that simulate the problem-solving capabilities of the human mind. Sophisticated AI technology has garnered significant attention in recent years in the domain of veterinary medicine. This review provides a comprehensive overview of the research dedicated to leveraging DL for diagnostic purposes within veterinary medicine. Our systematic review approach followed PRISMA guidelines, focusing on the intersection of DL and veterinary medicine, and identified 422 relevant research articles. After exporting titles and abstracts for screening, we narrowed our selection to 39 primary research articles directly applying DL to animal disease detection or management, excluding non-primary research, reviews, and unrelated AI studies. Key findings from the current body of research highlight an increase in the utilisation of DL models across various diagnostic areas from 2013 to 2024, including radiography (33% of the studies), cytology (33%), health record analysis (8%), MRI (8%), environmental data analysis (5%), photo/video imaging (5%), and ultrasound (5%). Over the past decade, radiographic imaging has emerged as most impactful. Various studies have demonstrated notable success in the classification of primary thoracic lesions and cardiac disease from radiographs using DL models compared to specialist veterinarian benchmarks. Moreover, the technology has proven adept at rec...