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Advancing fetal surveillance for early detection of fetal distress: using AI and novel physiological sensing

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)

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