Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging
This addresses the challenge of improving IVF success rates for infertility patients by automating embryo selection, though it is incremental as it builds on existing imaging techniques.
The paper tackled the problem of selecting embryos for IVF transfer by developing a data-driven system that predicts implantation probability from time-lapse imaging videos, resulting in a 12% increase in positive predictive value and a 29% increase in negative predictive value compared to embryologists.
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.