Enhancing Maternal and Child Health Outcomes Through Modelling and Machine Learning

Kahesh Dhuness and Charita Bhikha of the Council for Scientific and Industrial Research in South Africa will use statistical analysis and machine learning techniques on existing data to increase the effectiveness, as a triage tool, of Umbiflow, a Doppler ultrasound device used for antenatal screening. Umbiflow monitors umbilical artery blood flow to help determine the risk of impaired fetal growth. Statistical analysis of Umbiflow data will identify patterns of fetal risk across clinics and hospitals to help guide effective resource allocation. Application of machine learning to Umbiflow data from clinical trials will enable identification of complex patterns to improve determination of fetal risk and subsequent triage. They will also integrate a Large Language Model to support healthcare providers in evidence-based decision making using Umbiflow data.