Health TrialsbyHealthPanel

Quality Control of Ultrasound Images During Early Pregnancy Via AI

ConditionEarly Pregnancy
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age20+
SponsorChinese Academy of Sciences

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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 population

Women in early pregnancy

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Image quality control

    Other intervention

    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.

Treatment groups

400 Participants
are divided into 4 treatment groups
Group A: Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University1 intervention
Group B: Peking University Third Hospital1 intervention
Group C: Changsha Hospital for Maternal and Child Health Care1 intervention
Group D: Second Xiangya Hospital of Central South University1 intervention

Trial outcomes

Primary outcomes

1

PR curve of image quality control module

Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.

Time frame
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

Time frame
one month

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Chinese Academy of Sciences

Lead sponsor

Beijing Obstetrics and Gynecology Hospital

Collaborator

Peking University Third Hospital

Collaborator

Changsha Hospital for Maternal and Child Health Care

Collaborator

Second Xiangya Hospital of Central South University

Collaborator

Source ClinicalTrials.gov
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