Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

Journal: BMC pregnancy and childbirth
Published Date: Jan 16, 2022

Abstract

BACKGROUND: Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system?

Authors

  • Bo Huang
    Geriatrics Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China.
  • Shunyuan Zheng
    School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China.
  • Bingxin Ma
    Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
  • Yongle Yang
    Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
  • Shengping Zhang
    Department of Cognitive, Linguistic and Psychological Sciences, Brown Institute for Brain Science, Brown University, Providence, RI 02912; School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, Shandong, People's Republic of China; pwilf@psu.edu s.zhang@hit.edu.cn thomas_serre@brown.edu.
  • Lei Jin
    Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States.

Keywords

External Resources

Popular Topics
Recent Journals