Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction

Abstract

High content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, we explore whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contribute to the performance of machine learning-based implantation prediction. First, we show that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, we demonstrate that this unlabeled data boosts implantation prediction performance. Third, we characterize the cohort properties driving embryo prediction, especially those that rescued erroneous predictions. Our results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing inherent noise of the individual transferred embryo.