Fetal Weight, Machine Learning, Pregnancy, Pregnancy - Prenatal Testing
Conditions
Brief summary
Purpose: The primary objective of this study is to assess the diagnostic accuracy of an AI-enabled ultrasound tool for estimating fetal weight Participants: 1,000 pregnant individuals Procedures (methods): This prospective diagnostic accuracy study will enroll 1,000 pregnant individuals within one week of anticipated delivery. At a single visit, each participant will undergo two ultrasound assessments: (1) standardized sweeps for AI analysis (performed by both specialist and nonspecialist users), (2) specialist-performed fetal biometry.
Interventions
DIAGNOSTIC_TESTAI ultrasound diagnostic tool for fetal weight estimation
Participants will undergo study-specific transabdominal ultrasound acquisition using standardized abdominal sweeps of the gravid abdomen, guided by external maternal landmarks and saved as cineloop videos. The cineloop videos will be analyzed by a locked deep-learning AI diagnostic tool to generate an estimated fetal weight. The AI-generated estimate will be compared with specialist-performed fetal biometry and actual birth weight to evaluate diagnostic accuracy. The AI output is for research evaluation only and will not direct clinical management during the study.
Sponsors
University of North Carolina, Chapel Hill
Bill and Melinda Gates Foundation
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difference in Mean Absolute Percent Error (MAPE) in fetal weight estimation | Within 1 week of delivery, 24-42 weeks of gestation | Mean of pairwise differences in absolute percent error between the AI diagnostic tool (index test) and specialist biometry (clinical reference standard), compared against actual birthweight (ground truth). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of fetal weight estimates within 10% of actual birthweight | Within 1 week of delivery, 24-42 weeks of gestation | Between-method difference in the proportion of estimates within 10% of actual birthweight (ground truth) for the AI diagnostic tool (index test) versus specialist biometry (clinical reference standard). |
Countries
Canada, Rwanda, United States, Zambia
Contacts
CONTACTJeffrey R Stringer, MD
PRINCIPAL_INVESTIGATORJeffrey Stringer, MD
University of North Carolina, Chapel Hill
Outcome results
None listed