Project description

Current fetal monitoring technologies, such as cardiotocography (CTG), are often inaccurate at detecting fetal distress. This results in either delayed interventions, increasing the risk of brain injury, or unnecessary C-sections, contributing to surgical risks and increased healthcare costs. This project aims to fill this gap by developing new AI-powered software that non-invasively monitors fetal physiological signals to detect signatures corresponding to fetal distress (caused by hypoxia or infection/inflammation) for early detection. This is set to improve clinical decision-making, prevent perinatal brain injury, and ultimately save lives.