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CompletedNCT06314178Updated Jun 12, 2026

Comparing Artificial Intelligence and Standard Ultrasound Methods for Estimating Fetal Weight in Pregnancy. Patients Eligible for Inclusion Are Women With a Gestational Age Between 24-42 Weeks Undergoing a Growth Scan. The Image Data From the Scan Are Used to Calculate Fetal Weight.

An observational study in Pregnancy Complications, sponsored by Copenhagen Academy for Medical Education and Simulation. Completed at 1 site in Denmark. Open to female participants, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-06-12.

Sponsored by Copenhagen Academy for Medical Education and Simulation · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
283
Sex
Female
01

Study summary

The primary aim of this observational study is to compare the accuracy of two artificial intelligence (AI) models with the traditional Hadlock formula for estimating fetal weight from ultrasound scans performed in pregnant women between 24 and 42 weeks of gestation. The secondary aim is to investigate potential demographic bias in the AI models. The demographic factors examined include body mass index (BMI), parity, gestational age, maternal age, fetal sex, and the presence of preeclampsia.

Participants' ultrasound scans will be pseudonymized and securely stored on password-protected removable drives to ensure the protection of their identity and privacy. The ultrasound data will subsequently be transferred to the Technical University of Denmark (DTU), where the AI models will analyze the images to estimate fetal weight.

02

Conditions studied

  • Pregnancy Complications
03

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
Female
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study population

Department of Prenatal Examinations at Rigshospitalet, Copenhagen, Denmark.

Inclusion criteria

  • Women with gestational age between 24-42 weeks undergoing a third-trimester growth scan.

Exclusion criteria

Exclusion Criteria:

  • Women with multiple pregnancies.
04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
283 participants (actual)
Patient registry
No

Groups and cohorts

  • Pregnant women between 24-42 weeks of gestation

    No interventions

05

What researchers measure

Primary outcomes

  1. Comparing the accuracy of the Hadlock formula and the AI model

    The primary objective is to compare the accuracy of fetal weight estimation between the Hadlock formula and two deep learning models in clinical practice

    Time frame: From enrollment to the birth of the child

Secondary outcomes

  1. Demographic biases

    The secondary objective is to investigate whether the deep learning models show any demographic biases when estimating fetal growth in clinical practice. This is assessed by comparing the accuracy of the Hadlock formula and the deep learning models against the fetal weight at the time of the scan, which is estimated from the birth weight using the Marsal growth curve.

    Time frame: From enrollment to the birth of the child

06

Study locations

1 site
  • Copenhagen University Hospital, Rigshospitalet
    Copenhagen, Denmark
07

References and documents

Publications

  • Salomon LJ, Alfirevic Z, Da Silva Costa F, Deter RL, Figueras F, Ghi T, Glanc P, Khalil A, Lee W, Napolitano R, Papageorghiou A, Sotiriadis A, Stirnemann J, Toi A, Yeo G. ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol. 2019 Jun;53(6):715-723. doi: 10.1002/uog.20272. PubMed 31169958 ↗

Individual participant data

Plan to share: Undecided

08

Registry details

Key details

Study ID
NCT06314178
Lead sponsor
Copenhagen Academy for Medical Education and Simulation
Responsible party
Julie Leth-Petersen (Principal Investigator, Copenhagen Academy for Medical Education and Simulation) — Principal investigator
First posted
Mar 15, 2024
Start date
Jul 1, 2024
Primary completion
Dec 30, 2025
Completion
Dec 30, 2025
Last update
Jun 12, 2026

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.

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