Early PregnancyNCT06002412
Quality Control of Ultrasound Images During Early Pregnancy Via AI
An observational study of Early Pregnancy, run by Chinese Academy of Sciences at 4 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Early Pregnancy
- How many people
- 400 participants
- Where
- 4 sitesChina
- Study dates
- 1 Sep 2023to 30 Jul 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Women in early pregnancy
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Yali Zang, Ph.D contact
China4 sites, 4 recruiting
- Recruiting
Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
Beijing
Di Dong, di.dong@ia.ac.cn
- Recruiting
Peking University Third Hospital
Beijing
Di Dong, di.dong@ia.ac.cn
- Recruiting
Changsha Hospital for Maternal and Child Health Care
Changsha
Di Dong, di.dong@ia.ac.cn
- Recruiting
Second Xiangya Hospital of Central South University
Changsha
Di Dong, di.dong@ia.ac.cn
About this study
Official title: Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
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.
Read the 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.
- Conditions
- Early Pregnancy
- Being tested
- Image quality control
- Keywords
- Early Pregnancy, Ultrasound, Quality Control
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Image quality controlOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Image quality control | |
| 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. Receives: Other: Image quality control | |
| 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. Receives: Other: Image quality control | |
| 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. Receives: Other: Image quality control |
What is being measured
The primary outcome is the main question the study is designed to answer.
PR curve of image quality control module
Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.
Measured: one month
Secondary outcomes (1)
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
Measured: one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Aug 2023
- Study start1 Sep 2023
- Primary completion31 Dec 2023 (estimated)
- Study completion30 Jul 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Early PregnancyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese Academy of Sciences
- Sponsor type
- Other government
- 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
- Responsible party
- Principal investigator, Di Dong, Chinese Academy of Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06002412
- Sponsor's protocol ID
- CASMI005
- Status last verified
- Sep 2023
- First submitted
- 13 Aug 2023
- Last updated
- 8 Sep 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Study at a glance
From the registry record, last updated by the sponsor on 8 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
- ClinicalTrials.gov ID
- NCT06002412
- Acronym / sponsor ID
- CASMI005
- Conditions
- Early Pregnancy
- Keywords
- Early Pregnancy, Ultrasound, Quality Control
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Chinese Academy of SciencesOther government
- 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
- Study start
- 1 Sep 2023 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 30 Jul 2028 (estimated)
- First posted
- 21 Aug 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University | – | Other: Image quality controlBeijing 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 | – | Other: Image quality controlPeking 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 | – | Other: Image quality controlChangsha 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 | – | Other: Image quality controlSecond 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Women in early pregnancy
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Image quality controlThe 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. | Other | Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University, Changsha Hospital for Maternal and Child Health Care, Peking University Third Hospital, Second Xiangya Hospital of Central South University |
Primary outcome
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.
1 secondary outcome
The accuracy of intelligent analysis system in image quality control module
Time frame: one month
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Sep 2023.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Yali Zang, Ph.D contact
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Early Pregnancy, run by Chinese Academy of Sciences at 4 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Di Dong, Ph.D (+86 13811833760, di.dong@ia.ac.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
PR curve of image quality control module (time frame: one month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Sep 2023; AzTrials last refreshed its copy on 8 Nov 2026.