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
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.
411 studies on the registry are indexed under Pregnancy Complications; 113 are open to participants now.
This study's enrollment of 283 is below the median of 400 across 198 observational studies indexed under Pregnancy Complications.
Browse Pregnancy Complications studies →Copenhagen Academy for Medical Education and Simulation is the lead sponsor of 27 studies on the registry; 6 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Department of Prenatal Examinations at Rigshospitalet, Copenhagen, Denmark.
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Exclusion Criteria:
No interventions
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
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
Plan to share: Undecided
Ran from Jul 2024 to primary completion in Dec 2025, 17 months.
Of 113 non-industry observational studies under Pregnancy Complications, half reached primary completion within 60 months of starting (the median), counting those still running; a quarter within 30 and three quarters within 201. This study's 17 months is below that.
Counting every sponsor, half of 121 studies reached it within 60 months; a quarter within 30 and three quarters within 151.
How these figures are worked out, and which studies are left out of them.
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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Copenhagen Academy for Medical Education and Simulation