The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.

Journal: Reproductive biology and endocrinology : RB&E
PMID: 38987797

Abstract

OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.

Authors

  • Yu-Heng Xiao
    Chongqing Medical University, Chongqing, 400016, China.
  • Yu-Lin Hu
    The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China.
  • Xing-Yu Lv
    The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China.
  • Li-Juan Huang
    Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401147, China.
  • Li-Hong Geng
    The Reproductive Center, Sichuan Jinxin Xinan Women and Children's Hospital, Chengdu, Sichuan, 610011, China.
  • Pu Liao
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, China.
  • Yu-Bin Ding
    Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401147, China. dingyb@cqmu.edu.cn.
  • Chang-Chun Niu
    Chongqing Medical University, Chongqing, 400016, China. bright_star2000@sina.com.

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