Bovine embryo evaluation with machine learning: A tool for the future of assisted reproduction

In recent years, the integration of machine learning and com­puter vision (ML/CV) technologies has significantly advanced assisted reproductive technologies, and more specifically, the field of embryo analysis. In human in vitro fertilization (IVF), ML/CV has been used on time-lapse imaging systems to evalu­ate embryo morphological features to classify their quality and associated implantation potential. This technology has improved the accuracy and efficiency of embryo grading and selection by evaluating more parameters than the human eye can see under the microscope and removing the subjective na­ture from human evaluation. However, there has been little progress as it relates to ML/CV in the commercial bovine con­ventional embryo transfer and IVF industry. Thus, this study aimed to explore the potential of ML/CV automatic stage and grading techniques on bovine embryos. It was hypothesized that the machine learning model would outperform humans with little to no experience and perform equally or greater than humans with moderate or more experience staging and grading bovine embryos.