Artificial intelligence in human in vitro fertilization and embryology

Abstract

Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.

Keywords: Artificial intelligence; embryo evaluation; embryo selection; machine learning; ploidy prediction.