Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with s...

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Bibliographic Details
Main Authors: Qingsong Xi, Qiyu Yang, Meng Wang, Bo Huang, Bo Zhang, Zhou Li, Shuai Liu, Liu Yang, Lixia Zhu, Lei Jin
Format: Article
Language:English
Published: BMC 2021-04-01
Series:Reproductive Biology and Endocrinology
Subjects:
Artificial intelligence
Embryo selection
Machine learning
In vitro fertilization
In vitro fertilization prediction
Online Access:https://doi.org/10.1186/s12958-021-00734-z

Internet

https://doi.org/10.1186/s12958-021-00734-z

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