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

Pregnancy Related Clinical Trial

Official title: Z 32104 - Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age

Status Completed

Clinical Trial 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.

Clinical Trial 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 tablet. Study staff will enroll 400 pregnant volunteers prior to 14 completed gestational weeks and obtain accurate "ground truth" gestational age dating with standard ultrasound biometry, using the crown-rump length. These participants will then be asked to return for three follow-up visits, which will include a routine sonogram performed by a trained sonographer and the collection of a set of blind sweep cineloop videos using a low-cost, battery-operated device. The research will be conducted in Chapel Hill, North Carolina (at the University of North Carolina Hospital and/or sites associated with UNC OBGYN) and in Lusaka, Zambia (at the University Teaching Hospital or Kamwala District Health Centre). Approximately equal numbers of participants will be enrolled from each country. ;

Related Conditions & MeSH terms

• Gestational Age
• Machine Learning
• Pregnancy Related

• NCT number: NCT05433519
• Study type: Observational
• Source: University of North Carolina, Chapel Hill
• Status: Completed
• Start date: July 27, 2022
• Completion date: November 13, 2023