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

NCT05433519 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2024-05-08

No results posted yet for this study

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-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.

Conditions

  • Gestational Age
  • Machine Learning
  • Pregnancy Related

Sponsors & Collaborators

  • Bill and Melinda Gates Foundation

    collaborator OTHER

  • University of North Carolina, Chapel Hill

    lead OTHER

Principal Investigators

  • Jeff Stringer, MD · University of North Carolina

Eligibility

Min Age
18 Years
Max Age
59 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-07-27
Primary Completion
2023-05-31
Completion
2023-11-13

Countries

  • United States
  • Zambia

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 NCT05433519 on ClinicalTrials.gov