An automated approach for embryo morphokinetic analysis in small ruminants using machine learning and computer vision techniques

Assisted reproductive technologies have revolutionized the breeding practices for small ruminants, such as sheep and goats, by offering a more efficient and reliable method for pro­liferation of genetics from superior females. Yet, the success of these techniques relies heavily on accurate and timely embryo analysis, which has traditionally been a subjective process. Re­cently, embryo morphokinetic analysis has emerged as a new technique to determine the quality and developmental poten­tial of embryos. Traditional analysis of embryo morphokinetics has been performed in time-lapse embryo systems, many of which are not available for use in small ruminant embryology. Thus, the goal of this study was to determine if machine learn­ing and computer vision (ML/CV) could be applied to detect caprine embryo morphokinetic activity levels from short real-time videos. It was hypothesized that ML/CV will be able to de­tect differences in morphokinetic activity of these embryos.