Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

Journal: Reproductive biomedicine online
PMID: 36075848

Abstract

RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian hyper-response?

Authors

  • Xiaowen Liang
  • Jiamin Liang
    Medical UltraSound Computing (MUSIC) Lab, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Fengyi Zeng
    Department of Ultrasound Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Yan Lin
  • Yuewei Li
    Department of Ultrasound Medicine, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Kuan Cai
    Department of Ultrasound Medicine, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Dong Ni
  • Zhiyi Chen
    School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China.

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