Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols.

Journal: Journal of translational medicine
PMID: 40375277

Abstract

BACKGROUND: Individualizing follicle-stimulating hormone (FSH) dosing during controlled ovarian stimulation (COS) is critical for optimizing outcomes in assisted reproduction but remains difficult due to patient heterogeneity. Most existing models are limited to static predictions of initial doses and do not support real-time adjustments throughout stimulation.

Authors

  • Na Kong
    Nanjing Drum Tower Hospital, Drum Tower Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210008, Jiangsu, China.
  • Yu Xia
    Key Laboratory of Resources and Chemistry of Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, 430065, China.
  • Zhilong Wang
    College of Engineering, Cornell University, Ithaca, NY, 14853, USA.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Liyan Duan
    Center for Molecular Reproductive Medicine, Nanjing University, Nanjing, 2100 08, China.
  • Yingchun Zhu
    Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
  • Chenyang Huang
    Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
  • Guijun Yan
    Center for Molecular Reproductive Medicine, Nanjing University, Nanjing, 2100 08, China.
  • Jie Mei
    Department of Neurology and Department of Experimental Neurology, Neurocure Cluster of Excellence, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
  • Wujun Li
    National Institute of Healthcare Data Science at Nanjing University, Nanjing, 210008, Jiangsu, China. liwujun@nju.edu.cn.
  • Haixiang Sun
    Center for Reproductive Medicine and Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China. Electronic address: stevensunz@163.com.

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