Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction (Adv. Sci. 27/2023)

• Published in: Advanced science Vol. 10; no. 27
• 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: Weinheim John Wiley & Sons, Inc 01.09.2023; John Wiley and Sons Inc
• Subjects: Frontispiece

Machine Learning In article number 2207711 Assaf Zaritsky and colleagues enhance the performance of machine learning models to predict embryo implantation potential by using embryo cohort‐derived information. Using information encapsulated by the correlated “sibling” cohort embryos reduces the inher...