Condition: Fetal Weight · Pregnancy · Machine Learning · Sponsor: University of North Carolina, Chapel Hill
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.
This description comes directly from the study's public registry record.
Jeffrey R Stringer, MD · 919-962-4717 · jeff_stringer@unc.edu
Always discuss trial participation with your own doctor first.
| Ochsner Health | New Orleans, Louisiana, United States | Not Yet Recruiting |
| University of North Carolina | Chapel Hill, North Carolina, United States | Recruiting |
| University of Saskatchewan | Saskatoon, Saskatchewan, Canada | Not Yet Recruiting |
| University of Rwanda | Kigali, Rwanda | Not Yet Recruiting |
| University Teaching Hospital | Lusaka, Zambia | Not Yet Recruiting |
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Source record: clinicaltrials.gov/study/NCT07661433