Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction

• Published in: Advanced science Vol. 10; no. 27; p. e2207711
• Main Authors: Tzukerman, Noam, Rotem, Oded, Shapiro, Maya Tsarfati, Maor, Ron, Meseguer, Marcos, Gilboa, Daniella, Seidman, Daniel S, Zaritsky, Assaf
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.09.2023; John Wiley and Sons Inc; Wiley
• Subjects: Artificial intelligence; Cell division; cohort sibling embryos; Embryos; In vitro fertilization; Machine learning; Morphology; predicting embryo implantation potential; semi‐supervised learning; Siblings; Standard deviation; Uterus; in vitro fertilization; predicting embryo implantation potential; semi-supervised learning; machine learning; cohort sibling embryos
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202207711

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, whether, and to what extent the information encoded within "sibling" embryos from the same IVF cohort contributes to the performance of machine learning-based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.