Actively Recruiting
Quality Control of Ultrasound Images During Early Pregnancy Via AI
Led by Chinese Academy of Sciences · Updated on 2023-09-08
400
Participants Needed
4
Research Sites
256 weeks
Total Duration
On this page
Sponsors
C
Chinese Academy of Sciences
Lead Sponsor
B
Beijing Obstetrics and Gynecology Hospital
Collaborating Sponsor
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Quality Control of Ultrasound Images During Early Pregnancy Via AI
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Women in early pregnancy who have detailed personal information and ultrasound images
- Ultrasound images clearly showing the fetus's median sagittal, NT, and choroid plexus views
You will not qualify if you...
- 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
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 4 locations
1
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
Beijing, China
Actively Recruiting
2
Peking University Third Hospital
Beijing, China
Actively Recruiting
3
Changsha Hospital for Maternal and Child Health Care
Changsha, China
Actively Recruiting
4
Second Xiangya Hospital of Central South University
Changsha, China
Actively Recruiting
Research Team
D
Di Dong, Ph.D
CONTACT
Y
Yali Zang, Ph.D
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
4
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