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OpenTrials
Completed

NCT Number: NCT06314178

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.

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.

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Key information

Conditions

Pregnancy Complications Female Urogenital Diseases and Pregnancy Complications Urogenital Diseases

Sex eligibility

Female

Study type

Observational

Primary location

Copenhagen University Hospital, Rigshospitalet

Copenhagen, Denmark

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • Women with multiple pregnancies.

Treatment and study plan

Primary outcomes

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

    Time frame: From enrollment to the birth of the child

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

Secondary outcomes

  1. Demographic biases

    Time frame: From enrollment to the birth of the child

    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.

Sponsors and collaborators

Lead sponsor

Copenhagen Academy for Medical Education and Simulation

Other

Registry information

Official study title

A Prospective Silent Trial of Artificial Intelligence for Fetal Weight Estimation

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Mar 15, 2024
Registry last updated
Jun 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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