  The process of fertilizing a human egg outside the body in order to help
those suffering from infertility to conceive is known as in vitro fertilization
(IVF). Despite being the most effective method of assisted reproductive
technology (ART), the average success rate of IVF is a mere 20-40%. One step
that is critical to the success of the procedure is selecting which embryo to
transfer to the patient, a process typically conducted manually and without any
universally accepted and standardized criteria. In this paper we describe a
novel data-driven system trained to directly predict embryo implantation
probability from embryogenesis time-lapse imaging videos. Using retrospectively
collected videos from 272 embryos, we demonstrate that, when compared to an
external panel of embryologists, our algorithm results in a 12% increase of
positive predictive value and a 29% increase of negative predictive value.
