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| | | Article Name : | | | Mini Review: Artificial Intelligence in Domestic Animal Reproduction: Current Applications and Future | | Author Name : | | | Gerald Muça, Luigj Turmalaj | | Publisher : | | | Bridge Center | | Article URL : |  | | | Abstract : | | | Advances in digital technologies are rapidly reshaping veterinary medicine, with Artificial Intelligence (AI) emerging as a key tool in improving animal reproductive management. AI enhances breeding efficiency, fertility management, and reproductive health in cattle, pigs, sheep, dogs, and cats. This mini-review explores AI applications, including machine learning (ML), computer vision, and data analytics, which address challenges such as low fertility and genetic selection. Estrus detection leverages AI-powered sensors and vision systems to identify estrus with high accuracy (often >90%) through behavioral and physical indicators, reducing labor costs and missed heats in pigs and cows. Assisted reproductive technologies (ART) also benefit from AI in embryo viability assessment via image analysis, achieving accuracies of up to 79.3% in cattle in vitro fertilization (IVF). In controlled studies, AI sometimes outperforms human evaluators and enhances ultrasound-based pregnancy diagnosis in companion animals. The benefits include improved efficiency, cost reduction, and better welfare through non-invasive monitoring. However, challenges remain, including data privacy, rural accessibility, ethical considerations (such as bias and animal welfare), and the need for large, high-quality datasets. AI holds significant promise for sustainable breeding advancements through big data and hybrid models; nonetheless, integration with veterinary expertise and rigorous validation remains essential. | | Keywords : | | | Artificial Intelligence, Domestic Animals, Reproduction, Machine Learning, Estrus Detection |
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