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.
Study Type
OBSERVATIONAL
Enrollment
283
Copenhagen University Hospital, Rigshospitalet
Copenhagen, Denmark
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
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