Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation

University of North Carolina, Chapel Hill1,000 enrolled

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.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

Fetal Weight Pregnancy Machine Learning Pregnancy - Prenatal Testing

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. The AI output is for research evaluation only and will not direct clinical management during the study.

Eligibility

Sex: FEMALEMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (5)

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

Outcomes

Primary Outcomes

Difference in Mean Absolute Percent Error (MAPE) in fetal weight estimation

Mean of pairwise differences in absolute percent error between the AI diagnostic tool (index test) and specialist biometry (clinical reference standard), compared against actual birthweight (ground truth).

Time frame: Within 1 week of delivery, 24-42 weeks of gestation

Secondary Outcomes

Proportion of fetal weight estimates within 10% of actual birthweight

Between-method difference in the proportion of estimates within 10% of actual birthweight (ground truth) for the AI diagnostic tool (index test) versus specialist biometry (clinical reference standard).

Time frame: Within 1 week of delivery, 24-42 weeks of gestation

Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation

Overview

Conditions

Interventions

Eligibility

Locations (5)

Outcomes

Primary Outcomes

Secondary Outcomes

Central Contacts