Quality Control of Ultrasound Images During Early Pregnancy Via AI

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age20+
SponsorChinese Academy of Sciences

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

400 Participants
are divided into 4 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