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Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

NCT03398551 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 525

Last updated 2018-01-12

No results posted yet for this study

Summary

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows, to identify those at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities.

We will also compare the sensitivity and specificity of UmbiFlow device with the machine learning model in predicting adverse perinatal outcomes

Conditions

  • Perinatal Mortality
  • Neonatal Morbidities

Sponsors & Collaborators

  • Universitat Pompeu Fabra

    collaborator OTHER

  • Aga Khan University

    lead OTHER

Eligibility

Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2018-02-28
Primary Completion
2018-09-30
Completion
2018-12-31

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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 NCT03398551 on ClinicalTrials.gov