Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
NCT07661433 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000
Last updated 2026-07-08
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.
Conditions
- Fetal Weight
- Pregnancy
- Machine Learning
- Pregnancy - Prenatal Testing
Interventions
- DIAGNOSTIC_TEST
-
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. The AI output is for research evaluation only and will not direct clinical management during the study.
Sponsors & Collaborators
-
Bill and Melinda Gates Foundation
collaborator OTHER -
University of North Carolina, Chapel Hill
lead OTHER
Principal Investigators
-
Jeffrey Stringer, MD · University of North Carolina, Chapel Hill
Eligibility
- Min Age
- 18 Years
- Sex
- FEMALE
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2026-06-29
- Primary Completion
- 2026-12-31
- Completion
- 2026-12-31
- FDA Device
- Yes
Countries
- United States
- Canada
- Rwanda
- Zambia
Study Locations
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