Early Pregnancy
Conditions
Keywords
Early Pregnancy, Ultrasound, Quality Control
Brief 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.
Detailed description
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.
Interventions
OTHERImage 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.
Sponsors
Beijing Obstetrics and Gynecology Hospital
Peking University Third Hospital
Changsha Hospital for Maternal and Child Health Care
Second Xiangya Hospital of Central South University
Chinese Academy of Sciences
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
20 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| PR curve of image quality control module | one month | Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of intelligent analysis system in image quality control module | one month | The agreement between the prediction outcome of intelligent analysis system and the golden standard |
Countries
China
Contacts
Primary ContactDi Dong, Ph.D
Backup ContactYali Zang, Ph.D
Outcome results
None listed