Quality Control of Ultrasound Images During Early Pregnancy Via AI

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-09-01

Study Completion Date

2028-07-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Developing and Testing AI Models for Fetal Biometry and Amniotic Volume Assessment in Fetal Ultrasound Scans.

NCT05059093

Small for Gestational Age Infant Fetal Growth Restriction Oligohydramnios +1 more

COMPLETED

The Value of Prenatal Ultrasound in Complicated Twin Pregnancy and Its Correlation With Chromosomal Anomalies

NCT02732717

Complicated Twin Pregnancy

UNKNOWN

Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan

NCT05790473

Fetal Anomaly Brain Malformation

RECRUITING

AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection

NCT07261618

Intracranial Anomalies Ultrasound Imaging Artificial Intelligence

ACTIVE_NOT_RECRUITING

Machine Learning-based Anomaly Recognition System

NCT04897178

Fetal Anomaly

UNKNOWN

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

See the medical conditions and disease areas that this research is targeting or investigating.

Early Pregnancy

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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.