The Doppler Project: Machine learning for high-risk pregnancies in Pakistan

Title: Fetal Doppler for Antenatal Risk Stratification (Doppler project)

Team: Zahra Hoodbhoy (AKU), Babar Hasan (SIUT), Shazia Mohsin (SIUT), Devyani Chowdhury (Cardiology Care for Children, USA), Sergio Sanchez (Universitat Pompeu Fabra​), Josa Pratz (UPF), Bart Bijnens (UPF)

Objectives: Globally, Pakistan has the highest rate of stillbirths and early neonatal mortality (death within 1 week of life). Flow changes in several major arteries of the feto-placental circulation, as detected by Doppler echocardiography, may provide important information regarding fetal compromise which may lead to perinatal morbidity and mortality. 

The objective of this project is to build a machine learning algorithm on maternal and fetal characteristics along with Doppler waveforms to predict an adverse perinatal outcome.

Sites: Rehri Goth and Ibrahim Hyderi, Karachi
Timeline: Work is ongoing; anticipated completion, October 2024

Sponsor: Bill and Melinda Gates Foundation $2,525,000​

A 1:16 min video that summarises the Doppler project, prepared for dissemination. Credit: Rahim Sajwani/AKU Department of Paediatrics & Child Health

Dissemination, presentations:  

Hoodbhoy Z, Hasan B, Jehan F, Bijnens B, Chowdhury D. Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol. Gates open research. 2018;2.

Naz S, Hoodbhoy Z, Jaffar A, Kaleem S, Hasan BS, Chowdhury D, Gladstone M. Neurodevelopment assessment of small for gestational age children in a community-based cohort from Pakistan. Archives of disease in childhood. 2023 Apr 1;108(4):258-63.
Presentation and Panel Discussion at Grand Challenges Annual Meeting in Dakar, Senegal 2023

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