Background: Accurate and standardized assessment of sperm quality plays a crucial role in the success of breeding programs and the preservation of the genetic pool in dogs. On the other hand, standard techniques of sperm evaluation are often subjective, time-consuming, and filled with the possibility of human error. The purpose of this systematic review was to examine and compare the research that has already been conducted on the use of artificial intelligence (AI) as a technique that is precise, automated, and objective for evaluating a variety of canine sperm characteristics. Methods: In accordance with the PRISMA methodology, a thorough search was carried out in the databases of PubMed, Scopus, and Web of Science, using pertinent keywords up to March ۲۰۲۵. A selection of studies was made that used AI and machine learning (ML) in the process of analyzing canine sperm characteristics (including motility, concentration, and morphology). Standard techniques were used in order to evaluate the quality of the research, and information on the accuracy, methodologies, and most important results was retrieved. Results: Of the ۱۱ eligible studies that met the inclusion criteria, the results demonstrated the outstanding accuracy of AI algorithms, particularly convolutional neural networks (CNNs), in automatically assessing canine sperm parameters. These systems achieved an accuracy of between ۹۲% and ۹۷% in classifying sperm cells based on morphological health, demonstrating a very strong correlation with expert assessment. In motion analysis, deep learning-based tracking algorithms accurately classified progressive, non-progressive, and stationary motion types with a high sensitivity and specificity. They also calculated complex kinematic parameters, such as linear velocity (VCL) and acceleration (ALH), with an error of less than ۵%. Furthermore, AI demonstrated the potential to identify subtle and predictive patterns in sperm data, such as the relationship between the microenvironment and DNA integrity. Conclusion: The findings strongly suggest that AI offers a new, accurate, and automated paradigm for assessing canine sperm quality.
