Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.

Journal: BMC pregnancy and childbirth
PMID: 40165135

Abstract

BACKGROUND: Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI-IQA) system to audit images from the four key planes used in first-trimester scanning.

Authors

  • Xiaoyan Cao
    Ultrasound Department, Shenzhen Futian District Maternity & Child Healthcare Hospital, Shenzhen, Guangdong, 518016, China.
  • Binghan Li
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518073, China.
  • Yongsong Zhou
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518073, China.
  • Yan Cao
    School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Xindi Hu
    Shenzhen RayShape Medical Technology Co. Ltd., Shenzhen, China.
  • Chaoyu Chen
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
  • Shaokao Zhu
    Ultrasound Department, Shenzhen Futian District Maternity & Child Healthcare Hospital, Shenzhen, Guangdong, 518016, China.
  • Hengli Lin
    Ultrasound Department, Shenzhen Futian District Maternity & Child Healthcare Hospital, Shenzhen, Guangdong, 518016, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yuling Yan
    Ultrasound Department, Shenzhen Futian District Maternity & Child Healthcare Hospital, Shenzhen, Guangdong, 518016, China.
  • Tao Tan
    Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Dong Ni

Keywords

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