Quality Control of Ultrasound Images During Early Pregnancy Via AI

Study Overview
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Brief Summary
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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
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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.

Official Title
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Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy

Conditions
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Early Pregnancy

Intervention / Treatment
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* Other: Image quality control


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 20 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University<br>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. | Other: Image quality control<br>* 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.<br>|
| Peking University Third Hospital<br>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. | Other: Image quality control<br>* 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.<br>|
| Changsha Hospital for Maternal and Child Health Care<br>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. | Other: Image quality control<br>* 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.<br>|
| Second Xiangya Hospital of Central South University<br>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. | Other: Image quality control<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| PR curve of image quality control module | Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model. | one month | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of intelligent analysis system in image quality control module | The agreement between the prediction outcome of intelligent analysis system and the golden standard | one month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Early Pregnancy, Ultrasound, Quality Control