AI ultrasound tool to estimate fetal weight before delivery
Official title: Z 32503 - Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
Run by University of North Carolina, Chapel Hill · NCT07661433 · Data updated 2026-11-12
A AI-enabled ultrasound test is being tried in 1,000 pregnant people to see how accurately it can predict fetal weight compared with standard specialist measurements.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years and up |
| Sex | Women only |
| Enrollment | 1000 (estimated) |
| Sponsor | University of North Carolina, Chapel Hill Academic / other |
| Locations | 6 sites (New Orleans, Louisiana and 5 other locations) |
| Status | Recruiting |
| Started | June 29, 2026 |
| Main results expected | December 2026 |
| Study ends | December 2026 |
| Registry record updated | October 6, 2026 |
| Trial ID | NCT07661433 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are pregnant individuals age 18 or older with viable intrauterine pregnancy and an expected delivery within 1 week of procedures, not including multiple gestations or BMI ≥ 40 kg/m^2.
✕Probably not a fit
Patients with multiple gestations, known major fetal anomalies, or conditions that might affect participation may not benefit from the AI tool’s estimates.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective, diagnostic accuracy study evaluates an AI-assisted ultrasound method for estimating fetal weight. Participants will undergo two ultrasound assessments in a single visit: AI analysis from standardized sweeps (by both specialists and non-specialists) and traditional specialist-derived fetal biometry. The study aims to determine how closely the AI tool’s weight estimates match actual outcomes at delivery. Enrollment targets 1,000 pregnancies within one week of expected delivery, across multiple sites in the US. The lead sponsor is UNC Chapel Hill, with collaboration from the Gates Foundation and NCInnovation.
Why it matters
Potential benefit: If successful, the AI tool could provide faster, accessible, and potentially equally accurate fetal weight estimates at or near delivery.
How similar studies have gone: Prior studies in AI-assisted fetal imaging have shown promise in improving measurement consistency, though this exact approach with weight estimation is being tested in a prospective design.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 18 years of age or older * Viable intrauterine pregnancy * Delivery expected within one week of study procedures between 24 0/7 and 42 6/7 weeks, including participants with a scheduled induction or cesarean delivery on a known date, or those admitted in spontaneous labor * Ability and willingness to provide written informed consent * Willingness to comply with all study procedures Exclusion Criteria: * Maternal body mass index ≥ 40 kg/m\^2 * Multiple gestation (i.e., twins or higher order) * Known major fetal malformation or anomaly * Any maternal condition (medical, psychological, or social) that, in the opinion of the study team, may interfere with study participation or data integrity.
Where it's running
-
University of North Carolina
Chapel Hill, North Carolina, United States Recruiting -
University Teaching Hospital
Lusaka, Zambia Recruiting -
Ochsner Health
New Orleans, Louisiana, United States Not yet recruiting -
The Ohio State University
Columbus, Ohio, United States Not yet recruiting -
University of Saskatchewan
Saskatoon, Saskatchewan, Canada Not yet recruiting -
University of Rwanda
Kigali, Rwanda Not yet recruiting
Study contacts
- Principal investigator: Katelyn J Rittenhouse, MD — University of North Carolina, Chapel Hill
- Study coordinator: Katelyn J Rittenhouse, MD
- Email: katelyn.rittenhouse@unc.edu
- Phone: 919-962-4717
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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.