A Novel Approach Integrating Magnetic Resonance Imaging (MRI) Data and Artificial Intelligence for Predicting the Success Rate of Vaginal Delivery in Pregnant Women

Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18-35
SponsorSecond Affiliated Hospital of Wenzhou Medical University

About this trial

The aim of this study was to use MRI imaging to accurately scan the pregnant woman's pelvis and fetal skull, build a 3D model of them, and combine with artificial intelligence to develop an accurate tool to predict the success rate of vaginal delivery.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Full-term.

Single fetus, head first.

Pregnant women have vaginal couvade wishes.

Complete clinical data of pregnant women.

Disqualifiers

Pregnancy with serious medical and surgical diseases.

Abnormal fetal position (such as transverse, breech, etc.).

Twin or multiple pregnancies.

Vaginal couvade contraindications such as placenta previa.

Trial population

Full-term single live pregnant women who gave birth in the Department of Obstetrics of the Second Affiliated Hospital of Wenzhou Medical University from January 1, 2024 to December 31, 2026 were selected as the study population

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Mri scan of fetal head and pelvis

    Device

    The pelvic parameters and fetal head parameters of pregnant women were measured by MRI, including pelvic entrance plane, middle pelvic plane, pelvic outlet plane, pubic arch Angle, double parietal diameter, occipitofrontal diameter, and suboccipital fontanel diameter.

Treatment groups

200 Participants
are divided into 1 treatment group
Group A: Vaginal trial labor group1 intervention

Trial outcomes

Primary outcomes

1

The mode of delivery

Vaginal or cesarean delivery in the end

Time frame
during delivery

Secondary outcomes

Other outcomes

Sponsors and contacts

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This trial is not recruiting at the moment. You can still explore other options: