AI-based ultrasound and MRI to predict outcomes in placental-related fetal growth restriction
Official title: Establishment of a Cohort of Maternal Vascular Malperfusion-Related Fetal Growth Restriction (MVM-FGR) Based on an Etiology-Oriented Diagnostic Pathway: Artificial Intelligence-Assisted Multiparametric Ultrasound Prediction of Pregnancy Outcomes
Run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine · NCT07473739 · Data updated 2026-11-12
This project will test whether combining AI analysis of ultrasound and MRI with standard fetal monitoring can predict short- and long-term outcomes for pregnancies affected by early-onset, placental-related fetal growth restriction.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 20 Years to 43 Years |
| Sex | Women only |
| Enrollment | 300 (estimated) |
| Sponsor | Xinhua Hospital, Shanghai Jiao Tong University School of Medicine Academic / other |
| Locations | 1 site (Shanghai) |
| Status | Recruiting |
| Started | January 1, 2023 |
| Main results expected | November 30, 2026 |
| Study ends | November 30, 2028 |
| Registry record updated | March 16, 2026 |
| Trial ID | NCT07473739 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are pregnant people with singleton, early-onset placental insufficiency-related FGR—particularly those with abnormal umbilical artery Doppler—who choose to continue expectant management and can attend imaging and follow-up at the study site.
✕Probably not a fit
Patients whose FGR is due to fetal structural or genetic abnormalities, intrauterine infection, cases of multiple gestation with selective FGR, or those requiring immediate delivery are excluded and unlikely to benefit from this model.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective cohort will enroll singleton pregnancies with isolated early-onset placental insufficiency-related fetal growth restriction who continue expectant management. Investigators will collect novel intrauterine monitoring indicators along the fetal brain–placenta–heart axis together with conventional fetal surveillance (including Doppler) and imaging. Collected data will be used to train and validate predictive models that aim to estimate perinatal and neonatal adverse outcomes. Outcomes will include short-term perinatal events and longer-term neonatal endpoints to determine model performance and potential clinical utility.
Why it matters
Potential benefit: If successful, the predictive model could help clinicians identify high-risk fetuses earlier and better time interventions to reduce perinatal complications.
How similar studies have gone: Prior work shows Doppler and MRI markers correlate with outcomes and early machine-learning approaches show promise, but a fully integrated AI model along the brain–placenta–heart axis remains relatively novel and not yet widely validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Singleton pregnancy. * Isolated early-onset placental insufficiency-related fetal growth restriction (FGR), with priority given to cases with abnormal umbilical artery Doppler flow. * Pregnancies in which expectant management is continued. Exclusion Criteria: * Multiple pregnancy complicated by selective fetal growth restriction (sFGR). * FGR caused by fetal structural anomalies, genetic abnormalities, or intrauterine infection. * Twin pregnancy with intrauterine fetal demise (IUFD) of one fetus.
Where it's running
-
Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai, Shanghai, China
Shanghai, China Recruiting
Study contacts
- Study coordinator: Luming Sun, Prof
- Email: luming_sun@163.com
- Phone: +86 18321625166
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.