Enhancing frozen-thawed embryo transfer outcomes and treatment personalization through machine learning models.

Journal: Journal of assisted reproduction and genetics
Published Date: Aug 2, 2025

Abstract

BACKGROUND: Infertility affects millions globally, with significant social, emotional, and economic consequences. While frozen-thawed embryo transfer (FET) is a cornerstone of assisted reproductive technology, its clinical pregnancy success rates remain inconsistent (29.6-59.2%). Improving predictive accuracy and personalizing treatment strategies for FET outcomes could address critical unmet needs in reproductive medicine.

Authors

  • Junfeng Li
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China.
  • Hang Xing
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Yuan Chen
    Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032.
  • Yuqing Zhang
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.
  • Alix Hamon
    Department of Pediatrics, Women & Infants Hospital of Rhode Island, Alpert Medical School of Brown University, 101 Dudley Street, Providence, RI, 02905, USA.
  • Rongxiang Li
    Henan Key Laboratory of Fertility Protection and Aristogenesis, Department of Reproductive Center, Luohe Central Hospital, Luohe, 462000, Henan, China.
  • Shaozhe Yang
    Henan Key Laboratory of Fertility Protection and Aristogenesis, Department of Reproductive Center, Luohe Central Hospital, Luohe, 462000, Henan, China.
  • Xiuhong Fu
    Henan Key Laboratory of Fertility Protection and Aristogenesis, Department of Reproductive Center, Luohe Central Hospital, Luohe, 462000, Henan, China. fxh0430@outlook.com.

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