Z 32503 - Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
University of North Carolina, Chapel Hill
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.
Eligibility
- Age range
- 18+ years
- Sex
- Female
- 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,…
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.
Locations (4)
- Ochsner HealthNew Orleans, Louisiana
- University of SaskatchewanSaskatoon, Saskatchewan
- University of RwandaKigali
- University Teaching HospitalLusaka