Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI ultrasound diagnostic tool for fetal weight estimation.
- Who it may be relevant to
- Registry conditions: Fetal Weight, Pregnancy, Machine Learning, Pregnancy - Prenatal Testing. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States, Canada, Rwanda, Zambia
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Z 32503 - Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation
Overview
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
- 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 ev
Primary outcome measures
- Difference in Mean Absolute Percent Error (MAPE) in fetal weight estimation [Time frame: Within 1 week of delivery, 24-42 weeks of gestation]
Secondary outcome measures (1)
- Proportion of fetal weight estimates within 10% of actual birthweight [Time frame: Within 1 week of delivery, 24-42 weeks of gestation]
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 2 centers
- Ochsner Health — New Orleans
- University of North Carolina — Chapel Hill
Canada · 1 center
- University of Saskatchewan — Saskatoon
Rwanda · 1 center
- University of Rwanda — Kigali
Zambia · 1 center
- University Teaching Hospital — Lusaka
Identifiers
NCT: NCT07661433 · 25-3174 · 22-4067