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

Journal: Reproductive biology and endocrinology : RB&E
Published Date: Apr 5, 2021

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 sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.

Authors

  • Qingsong Xi
    Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, 430030, China.
  • Qiyu Yang
    Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, 430030, China.
  • Meng Wang
    State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150001, China.
  • Bo Huang
    Geriatrics Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Zhou Li
  • Shuai Liu
    Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China.
  • Liu Yang
    Department of Ultrasound, Hunan Children's Hospital, Changsha, China.
  • Lixia Zhu
    Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, 430030, China. zhulixia027@163.com.
  • Lei Jin
    Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States.

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