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.

This research is dedicated to integrating artificial intelligence technology to optimize the quality control process of early pregnancy ultrasonography. The ultrasound images involved primarily focus on the median sagittal section, NT section, and choroid plexus of the fetus during early pregnancy. In this regard, the investigators have collaborated with renowned medical institutions such as Beijing Obstetrics and Gynecology Hospital, Peking University Third Hospital, Changsha Hospital for Maternal and Child Health Care, and Second Xiangya Hospital of Central South University to retrospectively and prospectively collect a vast amount of early pregnancy fetal ultrasound image data. Based on this, the investigators plan to establish a model rooted in deep learning. This model will be capable of precisely identifying key anatomical regions in standard ultrasound scan images. Furthermore, by recognizing these anatomical structures, the model will determine whether the ultrasound image meets the standard scanning quality. This model is anticipated to serve as a powerful auxiliary tool in obstetric ultrasonography, enabling real-time assessment of ultrasound image quality, thereby significantly reducing the rates of missed and misdiagnosed fetal diseases such as Down Syndrome and neural system malformations.

Inclusion Criteria:

• 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 Criteria:

• 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.

Women in early pregnancy

Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.

Data will become available 6 months after study completion and will remain available for a period of 5 years.

Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.