Quality Control of Ultrasound Images During Early Pregnancy Via AI

Recruiting now

Conditions studied: Early Pregnancy

In brief

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.

Key facts

Study ID

NCT06002412

Run by

Chinese Academy of Sciences

People needed

400

Starts

2023-09-01

Expected to finish

2028-07-30

Last updated by the study team

2023-09-08

Who can join

Age: 20 and older. Sex: female. Healthy volunteers: accepted.

You may qualify if…

• 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.

You may not qualify if…

• 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.

Where it is running

• Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University — Beijing, China (enrolling)
• Peking University Third Hospital — Beijing, China (enrolling)
• Changsha Hospital for Maternal and Child Health Care — Changsha, China (enrolling)
• Second Xiangya Hospital of Central South University — Changsha, China (enrolling)

Full record on ClinicalTrials.gov

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