Artificial Intelligence Advances in Equine Medicine and Breeding | ||
| Egyptian Journal of Veterinary Sciences | ||
| Articles in Press, Corrected Proof, Available Online from 25 June 2025 PDF (954.82 K) | ||
| Document Type: Review Artical | ||
| DOI: 10.21608/ejvs.2025.371353.2735 | ||
| Authors | ||
| Mohamed Douifi* 1; Nabila Boukhalfa2 | ||
| 1Institute of Veterinary Sciences, University of Blida 1, Street Soumaa, BP270, 09000, Blida, Algeria. | ||
| 2Faculty of Sciences, University of Medea, Street Ouzera 26000, Algeria | ||
| Abstract | ||
| Equine health and breeding are vital areas where emerging technologies, particularly artificial intelligence (AI), are beginning to offer innovative solutions and applications. This study aimed to analyze recent literature regarding the applications, accuracy, and challenges of AI in equine health and breeding. In disease diagnosis, AI technologies, such as marker-less pose estimation, have proven effective in detecting lameness, accurately distinguishing between healthy horses and those with forelimb or hind limb problems. AI-driven deep learning algorithms have significantly enhanced the diagnosis of exercise-induced pulmonary hemorrhage (EIPH), consistently surpassing human annotators. In gastrointestinal health, AI models are excelling in classifying colic severity and determining the need for surgical intervention, resulting in improved treatment outcomes. AI is also transforming equine breeding by predicting breeding values based on genomic and phenotypic data. For instance, artificial neural networks (ANN) and support vector regression (SVR) have been utilized to forecast gait traits and optimize breeding strategies. Additionally, AI applications in equine imaging, such as video motion magnification for embryo selection, are enhancing reproductive efficiency. Regarding performance prediction, AI models have demonstrated high accuracy in for recasting race outcomes. Separately, monitoring grazing behaviors and food intake contributes to effective control of horses in pasture. However, challenges remain, including data quality issues, rresistance to integrating AI systems, and ethical concerns. The future of AI in equine care is promising, especially with the potential integration of AI and Internet of Things (IoT) devices for real-time monitoring and personalized medicine, which could greatly enhance equine health and performance. | ||
| Keywords | ||
| artificial intelligence; farming; equine; health; performance | ||
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