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.

Pregnancy Complications Clinical Trial

Official title:

Assessing Demographic Biases in Deep Learning Model for Fetal Growth Estimation in Clinical Practice

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06314178
• Other study ID #: p-2024-15469
• Status: Not yet recruiting
• Start date: June 1, 2024
• Est. completion date: July 30, 2024

Study information

• Verified date: March 2024
• Source: Copenhagen Academy for Medical Education and Simulation
• Contact: Julie Leth-Petersen
• Phone: +4526399590
• Email: julie.leth-petersen[@]regionh.dk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 300
• Est. completion date: July 30, 2024
• Est. primary completion date: July 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: N/A and older

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

• Women with multiple pregnancies.

Related Conditions & MeSH terms

• Pregnancy Complications

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Copenhagen Academy for Medical Education and Simulation

References & Publications (1)

Salomon LJ, Alfirevic Z, Da Silva Costa F, Deter RL, Figueras F, Ghi T, Glanc P, Khalil A, Lee W, Napolitano R, Papageorghiou A, Sotiriadis A, Stirnemann J, Toi A, Yeo G. ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol. 2019 Jun;53(6):715-723. doi: 10.1002/uog.20272. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	From enrollment to the birth of the child
Secondary	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.	From enrollment to the birth of the child