Quality Control of Ultrasound Images During Early Pregnancy Via AI
- Conditions
- Early Pregnancy
- Interventions
- Other: Image quality control
- Registration Number
- NCT06002412
- Lead Sponsor
- Chinese Academy of Sciences
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- Female
- Target Recruitment
- 400
- 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.
- 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.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University Image quality control Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps. Peking University Third Hospital Image quality control Peking University Third Hospital collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps. Changsha Hospital for Maternal and Child Health Care Image quality control Changsha Hospital for Maternal and Child Health Care collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps. Second Xiangya Hospital of Central South University Image quality control Second Xiangya Hospital of Central South University collects clinical information and ultrasound images of sagittal, NT and choroid plexus views of the fetus which was obtained from early pregnant women who underwent NT sweeps.
- Primary Outcome Measures
Name Time Method 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 Outcome Measures
Name Time Method 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
Trial Locations
- Locations (4)
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
🇨🇳Beijing, China
Peking University Third Hospital
🇨🇳Beijing, China
Second Xiangya Hospital of Central South University
🇨🇳Changsha, China
Changsha Hospital for Maternal and Child Health Care
🇨🇳Changsha, China