Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?

Journal: Reproductive biomedicine online
Published Date:

Abstract

RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data improve their predictive performance?

Authors

  • Yaoyu Zou
    Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, 252 Dalin Road Shanghai 200011, China.
  • Yingxia Pan
    Shanghai Biotecan Pharmaceuticals Co., Ltd. Shanghai, China; Shanghai Zhangjiang Institute of Medical Innovation Shanghai, China.
  • Naidong Ge
    Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, 252 Dalin Road Shanghai 200011, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
  • Ruihuan Gu
    Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, 252 Dalin Road Shanghai 200011, China.
  • Zhichao Li
    School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China. 2863@ecupl.edu.cn.
  • Jing Fu
    Shaoxing Second Hospital, 123 Yanan Road, Shaoxing, Zhejiang 312000, PR China.
  • Junhui Gao
    Shanghai Biotecan Pharmaceuticals Co., Ltd., 180 Zhangheng Road, Shanghai, 201204, China.
  • Xiaoxi Sun
    Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, 252 Dalin Road Shanghai 200011, China. Electronic address: xiaoxi_sun@aliyun.com.
  • Yijuan Sun
    Shanghai Ji Ai Genetics and IVF Institute, Obstetrics and Gynecology Hospital, Fudan University, 252 Dalin Road Shanghai 200011, China. Electronic address: yijuansss@163.com.