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

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...

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Detalles Bibliográficos
Autores principales: Tzukerman, Noam, Rotem, Oded, Shapiro, Maya Tsarfati, Maor, Ron, Meseguer, Marcos, Gilboa, Daniella, Seidman, Daniel S., Zaritsky, Assaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Frontispiece
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520614/
http://dx.doi.org/10.1002/advs.202370183
Descripción
Sumario: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 inherent noise of the individual transferred embryo associated with its implantation uncertainty. [Image: see text]

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