Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: A study protocol

Zahra Hoodbhoy, Babar Hasan, Fyezah Jehan, Bart Bijnens, Devyani Chowdhury

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Background: In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. Methods: This will be prospective one group cohort study which will be conducted in Ibrahim Hyderi, a peri-urban settlement in south east of Karachi. The eligibility criteria include pregnant women between 22-34 weeks who reside in the study area. Once enrolled, in addition to the performing fetal ultrasound to obtain Dopplers, data on socio-demographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women. Discussion: The machine learning approach for predicting adverse perinatal outcomes obtained from the current study will be validated in a larger population at the next stage. The data will allow for early interventions to improve perinatal outcomes.

Original languageEnglish
Article number8
JournalGates Open Research
Publication statusPublished - 2018


  • Adverse outcomes
  • Echocardiography
  • Machine learning
  • Pregnancy


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