Status:
COMPLETED
Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
Lead Sponsor:
University of North Carolina, Chapel Hill
Collaborating Sponsors:
Bill and Melinda Gates Foundation
Conditions:
Gestational Age
Machine Learning
Eligibility:
FEMALE
18-59 years
Brief Summary
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...
Detailed Description
The primary purpose of this research is to assess the diagnostic accuracy of the FAMLI Technology, a novel machine learning-based tool for gestational age assessment that can run on a smart phone or t...
Eligibility Criteria
Inclusion
- Inclusion Criteria:
- 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
- Exclusion criteria:
- 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.
Exclusion
Key Trial Info
Start Date :
July 27 2022
Trial Type :
OBSERVATIONAL
Allocation :
ACTUAL
End Date :
November 13 2023
Estimated Enrollment :
400 Patients enrolled
Trial Details
Trial ID
NCT05433519
Start Date
July 27 2022
End Date
November 13 2023
Last Update
May 8 2024
Active Locations (2)
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1
University of North Carolina
Chapel Hill, North Carolina, United States, 27599
2
University Teaching Hospital
Lusaka, Zambia