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

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 curren...

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Bibliographic Details
Main Authors: Noam Tzukerman, Oded Rotem, Maya Tsarfati Shapiro, Ron Maor, Marcos Meseguer, Daniella Gilboa, Daniel S. Seidman, Assaf Zaritsky
Format: Article
Language:English
Published: Wiley 2023-09-01
Series:Advanced Science
Subjects:
cohort sibling embryos
in vitro fertilization
machine learning
predicting embryo implantation potential
semi‐supervised learning
Online Access:https://doi.org/10.1002/advs.202207711

Internet

https://doi.org/10.1002/advs.202207711

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