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.
Essential criteria:
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords
women's health, artificial intelligence, newborn health, birth asphyxia, fetal hypoxia, fetal infection, inflammation
School
School of Clinical Sciences at Monash Health / Hudson Institute of Medical Research
School of Clinical Sciences at Monash Health / Hudson Institute of Medical Research » Obstetrics and Gynaceology
School of Clinical Sciences at Monash Health / Hudson Institute of Medical Research » Paediatrics
Available options
PhD/Doctorate
Masters by research
Honours
BMedSc(Hons)
Joint PhD/Exchange Program
Time commitment
Full-time
Top-up scholarship funding available
No
Physical location
Monash Health Translation Precinct (Monash Medical Centre)