Predicting the risk of a high proportion of three/multiple pronuclei (3PN/MPN) zygotes in individual IVF cycles using comparative machine learning algorithms.

Journal: European journal of obstetrics, gynecology, and reproductive biology
PMID: 39826276

Abstract

BACKGROUND: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles.

Authors

  • Pingping Hong
    Reproductive Medicine Center, The Third Affiliated Hospital of Sun Yat-sen University, 6# East Longkou Road, Gangding, Tianhe District, Guangzhou 510630 China. Electronic address: hongpp3@mail.sysu.edu.cn.
  • Yaxin Lu
    Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
  • Haiyang Li
    Department of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, China.
  • Zifeng Liu
    Shandong Yankuang Group Changlong Cable Manufacturing Co., Ltd, Jining, China.
  • Jianpin Ou
    Reproductive Medicine Center, The Third Affiliated Hospital of Sun Yat-sen University, 6# East Longkou Road, Gangding, Tianhe District, Guangzhou 510630 China. Electronic address: oujp3@mail.sysu.edu.cn.
  • Tao Li
    Department of Emergency Medicine, Jining No.1 People's Hospital, Jining, China.
  • Yimin Shu
    Life IVF Center, Irvine, CA, United States; Department of Obstetrics and Gynecology, The University of Kansas Health System, Kansas City, KS, United States.

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