ABSTRACT
This chapter reviewS the current landscape of AI in gamete and embryo selection in the in vitro fertilization laboratory. It explores gamete selection methods driven by ML. The chapter takes a high-level look at the landscape for AI systems for embryo selection. Since the early 2010s, the collection of large data sets and increases in computing power have enabled numerous successes in the field of artificial intelligence (AI). To date, almost all work in AI for embryo selection has involved the use of supervised learning to predict a clinically relevant outcome from input data. For instance, it may be argued that automated embryo selection systems should be trained using live birth data since a live birth is the end goal of the ART cycle. Many early studies examining the potential for the use of AI in embryo selection focused on single static images.
