Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing
The proposed AI-IQA system proved to be a highly accurate and efficient method of automatically auditing first-trimester scanning image quality, providing precise and rapid key plane quality control. This tool can also assist radiologists with different levels of experience to improve the image quality.
Authors
- Xindi Hu (ORCID: https://orcid.org/0000-0003-4478-6263)
- Lin Wang (ORCID: https://orcid.org/0009-0004-8358-0668)
- Kai Chen (ORCID: https://orcid.org/0000-0002-3930-8294)
- Yuling Yan (ORCID: https://orcid.org/0000-0002-2765-8350)
- Dong Ni (ORCID: https://orcid.org/0000-0002-9146-6003)
- Siwei Zhu (ORCID: https://orcid.org/0009-0002-5951-6784)
- Heng-Li Lin
- Xin Yang (ORCID: https://orcid.org/0000-0001-6252-1061)
- Tao Wang (ORCID: https://orcid.org/0000-0002-9647-5718)
- Tao Tan (ORCID: https://orcid.org/0000-0001-5403-0887)
- Binghan Li (ORCID: https://orcid.org/0009-0001-9788-7597)
- Yan Cao (ORCID: https://orcid.org/0000-0001-5510-9773)
- Yongsong Zhou (ORCID: https://orcid.org/0009-0000-2259-0070)
- Xiaoyan Cao
Institutions
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Shenzhen University Health Science Center (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1186/s12884-025-07485-4
- Citations
- 4
- Primary Topic
- Fetal and Pediatric Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 13.98
Funders
- National Natural Science Foundation of China
- Science and Technology Planning Project of Guangdong Province