RECRUITING Not applicable ClinicalTrials.gov

Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation

Sponsor: University of North Carolina, Chapel Hill • ClinicalTrials.gov ID: NCT07661433

Registry updated: 2026-10-06

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Description

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

AI ultrasound diagnostic tool for fetal weight estimation (Diagnostic test)
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.

Eligibility 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.

Conditions

Fetal Weight Pregnancy Machine Learning Pregnancy - Prenatal Testing

Locations

Ochsner Health — New Orleans, Louisiana, United States
University of North Carolina — Chapel Hill, North Carolina, United States
The Ohio State University — Columbus, Ohio, United States
University of Saskatchewan — Saskatoon, Saskatchewan, Canada
University of Rwanda — Kigali, Rwanda
University Teaching Hospital — Lusaka, Zambia

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Official Source

Data sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

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