Automated assessment of endometrial receptivity for screening recurrent pregnancy loss risk using deep learning-enhanced ultrasound and clinical data.

Journal: Frontiers in physiology
Published Date: Dec 24, 2024

Abstract

BACKGROUND: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.

Authors

  • Shanling Yan
    Department of Ultrasound, Deyang People's Hospital, Deyang, Sichuan, China.
  • Fei Xiong
    Department of Ultrasound, Deyang People's Hospital, Deyang, Sichuan, China.
  • Yanfen Xin
    Department of Ultrasound, Deyang People's Hospital, Deyang, Sichuan, China.
  • Zhuyu Zhou
    Department of Ultrasound, Deyang People's Hospital, Deyang, Sichuan, China.
  • Wanqing Liu
    Department of Obstetrics and Gynecology, Deyang People's Hospital, Deyang, Sichuan, China.

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