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Assessing Demographic Biases in Deep Learning Model for Fetal Growth Estimation in Clinical Practice. Patients Eligible for Inclusion Are Women with a Gestational Age Between 24-42 Weeks Undergoing a Third-trimester Growth Scan. the Image Data from the Scan Are Used to Calculate Fetal Weight.

NCT06314178 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 185

Last updated 2024-12-04

No results posted yet for this study

Summary

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.

Conditions

  • Pregnancy Complications

Sponsors & Collaborators

  • Copenhagen Academy for Medical Education and Simulation

    lead OTHER

Eligibility

Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-07-01
Primary Completion
2024-08-30
Completion
2024-11-30

Countries

  • Denmark

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06314178 on ClinicalTrials.gov