Quality Control of Ultrasound Images During Early Pregnancy Via AI
Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
Chinese Academy of Sciences
400 participants
Sep 1, 2023
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria2
- 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.
Exclusion Criteria3
- 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.
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Interventions
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)
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NCT06002412