ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY >

2025 , Vol. 9 >Issue 4: 449 - 456

DOI: https://doi.org/10.26599/AUDT.2025.250095

Current Applications of Artificial Intelligence in Obstetric Ultrasound

  • Li Yanran ,
  • Cui Yuanjie ,
  • Wu Qingqing ,
  • Zhang Na
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  • aDepartment of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China
Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, No.251 Yaojiayuan Road, Chaoyang District, Beijing, 100026, China (Qingqing Wua, Na Zhang)e-mail: qingqingwu@ccmu.edu.cn (QQ W);
nana8022@ccmu.edu.cn (N Z)

Received date: 2025-09-07

  Revised date: 2025-09-14

  Accepted date: 2025-10-02

  Online published: 2025-11-06

Copyright

2576-2508/© AUDT 2025
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Abstract

Artificial Intelligence (AI) technology has made remarkable progress in fetal ultrasound examinations, particularly excelling in fetal growth monitoring, organ function assessment, and early disease diagnosis. By automating the analysis of fetal ultrasound images, AI can accurately measure fetal biometric parameters and assist in diagnosing issues such as fetal growth restriction and organ developmental abnormalities. It demonstrates significant application potential in evaluating multiple organ systems including the fetal lungs, nervous system, cardiovascular system, and placenta, substantially enhancing the efficiency and accuracy of prenatal screening. This paper aims to review the current status of AI applications in obstetric ultrasound, while also exploring its limitations and future prospects.

Key words： Artificial intelligence; Obstetric ultrasound; Deep learning; Convolutional neural networks; Machine learning

Cite this article

Li Yanran , Cui Yuanjie , Wu Qingqing , Zhang Na . Current Applications of Artificial Intelligence in Obstetric Ultrasound[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2025 , 9(4) : 449 -456 . DOI: 10.26599/AUDT.2025.250095

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