TY - JOUR AU - Xi, Qingsong AU - Yang, Qiyu AU - Wang, Meng AU - Huang, Bo AU - Zhang, Bo AU - Li, Zhou AU - Liu, Shuai AU - Yang, Liu AU - Zhu, Lixia AU - Jin, Lei PY - 2021 DA - 2021/04/05 TI - Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study JO - Reproductive Biology and Endocrinology SP - 53 VL - 19 IS - 1 AB - 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 sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. SN - 1477-7827 UR - https://doi.org/10.1186/s12958-021-00734-z DO - 10.1186/s12958-021-00734-z ID - Xi2021 ER -