About this trial
This research integrates artificial intelligence to enhance early pregnancy ultrasonography quality control, focusing on specific fetal sections. In collaboration with prominent medical institutions, the investigators have amassed extensive fetal ultrasound data. The investigators aim to develop a deep learning model that can accurately identify essential anatomical areas in ultrasound images and evaluate their quality. This tool is expected to significantly decrease misdiagnoses of conditions like Down Syndrome and neural system deformities by ensuring real-time image quality assessment.
Eligibility criteria
Qualifiers
Women in early pregnancy who have detailed personal information and ultrasound images.
The ultrasound images should clearly show the fetus's median sagittal, NT, and choroid plexus views.
Disqualifiers
Ultrasound images from women in mid to late pregnancy.
Ultrasound images that are unclear or blurry, making evaluation difficult.
Women who did not provide complete personal and medical information during the ultrasound scan.
Trial design
Treatments tested in this trial
- Image quality control
Treatment groups
Sponsors and collaborators
Chinese Academy of Sciences
Lead sponsor
Beijing Obstetrics and Gynecology Hospital
Collaborator
Peking University Third Hospital
Collaborator
Changsha Hospital for Maternal and Child Health Care
Collaborator
Second Xiangya Hospital of Central South University
Collaborator