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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06044129

Enrollment

200

Registered

2023-09-21

Start date

2024-01-01

Completion date

2026-12-31

Last updated

2023-09-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Vaginal Delivery

Brief summary

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.

Interventions

DEVICEMri scan of fetal head and pelvis

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.

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to 35 Years

Healthy volunteers

Yes

Inclusion criteria

* Full-term. * Single fetus, head first. * Pregnant women have vaginal couvade wishes. * Complete clinical data of pregnant women.

Exclusion criteria

* 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.

Design outcomes

Primary

Measure	Time frame	Description
The mode of delivery	during delivery	Vaginal or cesarean delivery in the end

Countries

China

Contacts

Primary ContactYing Hua

wzfeyhy1015@126.com+8613676403165

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026