Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age

Completed

Conditions studied: Gestational Age, Machine Learning, Pregnancy Related

In brief

This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.

Key facts

Study ID

NCT05433519

Run by

University of North Carolina, Chapel Hill

People needed

400

Starts

2022-07-27

Expected to finish

2023-11-13

Last updated by the study team

2024-05-08

Who can join

Age: 18 and older, up to 59. Sex: female. Healthy volunteers: not accepted.

You may qualify if…

• 18 years of age or older
• viable intrauterine pregnancy at less than 14 0/7 weeks of gestation
• ability and willingness to provide written informed consent
• intent to remain in current geographical area of residence for the duration of study
• willingness to adhere to study procedures

You may not qualify if…

• maternal body mass index = 40 kg/m\^2
• multiple gestation (i.e., twins or higher order)
• major fetal malformation or anomaly
• any other condition (social or medical) that, in the opinion of the study staff, would make study participation unsafe or complicate data interpretation.

Where it is running

• University of North Carolina — Chapel Hill, North Carolina, United States
• University Teaching Hospital — Lusaka, Zambia

Full record on ClinicalTrials.gov

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