MedTrace Logo

Quality Control of Ultrasound Images During Early Pregnancy Via AI

NCT06002412 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2023-09-08

No results posted yet for this study

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.

Conditions

  • Early Pregnancy

Interventions

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.

Sponsors & Collaborators

  • Beijing Obstetrics and Gynecology Hospital

    collaborator OTHER

  • Peking University Third Hospital

    collaborator OTHER

  • Changsha Hospital for Maternal and Child Health Care

    collaborator OTHER

  • Second Xiangya Hospital of Central South University

    collaborator OTHER

  • Chinese Academy of Sciences

    lead OTHER_GOV

Eligibility

Min Age
20 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-09-01
Primary Completion
2023-12-31
Completion
2028-07-30

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06002412 on ClinicalTrials.gov