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

Here, we explore whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contribute tothe performance of machine learning-based implantation prediction. First, we show that theimplantation outcome is correlated with attributes derived from the siblings cohort. Second, wedemonstrate that this unlabeled data boosts implantation prediction performance. Third, wecharacterize the siblings cohort properties driving embryo prediction, especially those thatrescued erronous predictions. -- From the abstract