Status:

UNKNOWN

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

Lead Sponsor:

Aga Khan University

Collaborating Sponsors:

Universitat Pompeu Fabra

Conditions:

Perinatal Mortality

Neonatal Morbidities

Eligibility:

FEMALE

Brief 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, cerebra...

Detailed Description

Pakistan is one of the countries where stillbirth rate (43/1000 total births) and neonatal mortality rate (55/1000 live births) are among the highest in the world. The figures for perinatal mortality ...

Eligibility Criteria

Inclusion

  • Pregnant woman coming to the ultrasound clinic between 22-34 weeks of gestation.
  • Written informed consent
  • Resident of the study area

Exclusion

  • Multiple gestation
  • Known congenital anomaly in the fetus or newborn
  • Refusal for the ultrasound
  • Poor echocardiographic images for Doppler acquisition

Key Trial Info

Start Date :

February 1 2018

Trial Type :

OBSERVATIONAL

Allocation :

ESTIMATED

End Date :

December 1 2018

Estimated Enrollment :

525 Patients enrolled

Trial Details

Trial ID

NCT03398551

Start Date

February 1 2018

End Date

December 1 2018

Last Update

January 12 2018

Active Locations (0)

Enter a location and click search to find clinical trials sorted by distance.

Page 1 of 0 (0 locations)

No Results Found

We couldn’t find results for the location/zipcode entered or within the selected range. Please check your input or adjust your search.

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes | DecenTrialz