The goal of this observational study is to compare a new artificial intelligence (AI) feedback tool with the traditional method for estimating fetal weight during ultrasound scans on pregnant women between 24-42 weeks of gestation. The study aims to investigate the presence of demographic bias in the AI model. The demographic factors examined in the study include Body Mass Index (BMI), the number of births, fetal age, mother\'s age, fetal sex, and the presence of preeclampsia. Moreover, the study will compare the accuracy of the AI model and the Hadlock model, a fetal growth formula, in estimating fetal weight. Participants will have their ultrasound scans pseudonymized and securely stored on password-protected removable drives, ensuring their identity and privacy are maintained. Afterward, the ultrasound data will be sent to the Technical University of Denmark (DTU), where the AI model will analyze the images to estimate fetal weight.
Study Type
OBSERVATIONAL
Enrollment
185
Copenhagen University Hospital, Rigshospitalet
Copenhagen, Denmark
Demographic biases
The primary objective is to investigate potential demographic biases inherent in the deep learning model developed for estimating fetal growth in clinical practice. This is achieved by comparing the relative error between fetal weight at scan time (this value is extrapolated from the birth weight using the Marsal growth curve) and estimations from the Hadlock formula and the deep learning model.
Time frame: From enrollment to the birth of the child
Comparing the accuracy of the Hadlock formula and the AI model
The secondary objective is to compare the accuracy of fetal weight estimation between the Hadlock formula and a deep learning model in clinical practice using a paired t-test.
Time frame: From enrollment to the birth of the child
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