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Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
Sponsor: 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.
Official title: Z 32503 - Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
Key Details
Gender
FEMALE
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
1000
Start Date
2026-06-29
Completion Date
2026-12
Last Updated
2026-10-06
Healthy Volunteers
No
Interventions
AI 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.
Locations (6)
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