Recruiting

AI-Enabled Ultrasound

Sponsor:

University of North Carolina, Chapel Hill

Code:

NCT07661433

Conditions

Fetal Weight

Pregnancy

Machine Learning

Pregnancy - Prenatal Testing

Eligibility Criteria

Sex: Female

Age: 18+

Healthy Volunteers: Not accepted

Interventions

AI ultrasound diagnostic tool for fetal weight estimation

Study Details

Brief 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

Study ID

NCT07661433

Start date

Jun 29, 2026

Status verified date

Jun, 2026

Completion date

Dec, 2026

Anticipated

Primary completion date

Dec, 2026

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: Female

Age: 18+

Healthy Volunteers: Not accepted

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.

Study Design

Enrollment

1000 participants

Anticipated

Interventions and Outcome Measures

Arms

Pregnant Women within One Week of Delivery

Participants receive a standardized ultrasound sweep protocol and specialist-performed fetal biometry.

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.

Primary outcome measure

  • Difference in Mean Absolute Percent Error (MAPE) in fetal weight estimation [ Time Frame: Within 1 week of delivery, 24-42 weeks of gestation ]

Central Contacts and Locations

Central contacts

Jeffrey R Stringer, MD

919-962-4717jeff_stringer@unc.edu

Locations

University of North Carolina

Recruiting

Chapel Hill, North Carolina, United States, 27516

Contacts

Katelyn J Rittenhouse, MD

919-966-5281katelyn_rittenhouse@med.unc.edu

Jeffrey S.A. Stringer, MD

919-966-5281jeffrey_stringer@med.unc.edu

More Information

Sponsor

University of North Carolina, Chapel Hill

Last update posted

Jul 8, 2026

Last verified

Jun, 2026

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by University of North Carolina, Chapel Hill on 2026-07-08.