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Quality Control of Ultrasound Images During Early Pregnancy Via AI

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ClinicalTrials.gov Identifier: NCT06002412
Recruitment Status : Recruiting
First Posted : August 21, 2023
Last Update Posted : September 8, 2023
Sponsor:
Collaborators:
Beijing Obstetrics and Gynecology Hospital
Peking University Third Hospital
Changsha Hospital for Maternal and Child Health Care
Second Xiangya Hospital of Central South University
Information provided by (Responsible Party):
Di Dong, Chinese Academy of Sciences

Study Description
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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.

Condition or disease Intervention/treatment
Early Pregnancy Other: Image quality control

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.
Study Design
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Study Type : Observational
Estimated Enrollment : 400 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
Actual Study Start Date : September 1, 2023
Estimated Primary Completion Date : December 31, 2023
Estimated Study Completion Date : July 30, 2028

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Pregnancy

Groups and Cohorts
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Group/Cohort Intervention/treatment
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
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
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.
Peking University Third Hospital
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
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.
Changsha Hospital for Maternal and Child Health Care
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
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.
Second Xiangya Hospital of Central South University
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
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.


Outcome Measures
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Primary Outcome Measures :
  1. PR curve of image quality control module [ Time Frame: one month ]
    Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.


Secondary Outcome Measures :
  1. The accuracy of intelligent analysis system in image quality control module [ Time Frame: one month ]
    The agreement between the prediction outcome of intelligent analysis system and the golden standard


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   20 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
Women in early pregnancy
Criteria

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.
Contacts and Locations
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06002412


Contacts
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Contact: Di Dong, Ph.D +86 13811833760 di.dong@ia.ac.cn
Contact: Yali Zang, Ph.D yali.zang@ia.ac.cn

Locations
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China
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University Recruiting
Beijing, China
Contact: Di Dong       di.dong@ia.ac.cn   
Peking University Third Hospital Recruiting
Beijing, China
Contact: Di Dong       di.dong@ia.ac.cn   
Changsha Hospital for Maternal and Child Health Care Recruiting
Changsha, China
Contact: Di Dong       di.dong@ia.ac.cn   
Second Xiangya Hospital of Central South University Recruiting
Changsha, China
Contact: Di Dong       di.dong@ia.ac.cn   
Sponsors and Collaborators
Chinese Academy of Sciences
Beijing Obstetrics and Gynecology Hospital
Peking University Third Hospital
Changsha Hospital for Maternal and Child Health Care
Second Xiangya Hospital of Central South University
More Information
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Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
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Responsible Party: Di Dong, Professor, Chinese Academy of Sciences
ClinicalTrials.gov Identifier: NCT06002412    
Other Study ID Numbers: CASMI005
First Posted: August 21, 2023    Key Record Dates
Last Update Posted: September 8, 2023
Last Verified: September 2023
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: 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.
Supporting Materials: Study Protocol
Statistical Analysis Plan (SAP)
Analytic Code
Time Frame: Data will become available 6 months after study completion and will remain available for a period of 5 years.
Access Criteria: 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.
URL: http://www.radiomics.net.cn/

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Di Dong, Chinese Academy of Sciences:
Early Pregnancy
Ultrasound
Quality Control