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Quality Control of Ultrasound Images During Early Pregnancy Via AI
Sponsor: Chinese Academy of Sciences
Summary
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.
Official title: Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
Key Details
Gender
FEMALE
Age Range
20 Years - Any
Study Type
OBSERVATIONAL
Enrollment
400
Start Date
2023-09-01
Completion Date
2028-07-30
Last Updated
2023-09-08
Healthy Volunteers
Yes
Conditions
Interventions
Image quality control
The investigators identify the region of interest in the relevant section to give a conclusion on whether the image is standard or not, guiding clinicians to standardize the operation, and reducing the rate of misdiagnosis and underdiagnosis.
Locations (4)
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
Beijing, China
Peking University Third Hospital
Beijing, China
Changsha Hospital for Maternal and Child Health Care
Changsha, China
Second Xiangya Hospital of Central South University
Changsha, China