Olivia Ng LinkedIn
PhD Candidate, University of New South Wales, Australia.
PhD Topic:
"Clinical Predictive Tool for Arteriovenous Fistula (AVF) Failure"
Research interests: Computational Fluid Dynamics (CFD), Biomechanical Engineering, Blood Flow, Data Analysis
Email: olivia.ng@unsw.edu.au
Education/Employment
2018-present PhD Mechanical Engineering, UNSW, Sydney
2016-present Casual Academic, School of Mechanical Engineering, UNSW, Sydney
2014-2017 BEng Honours Class 1 (Mechanical Engineering), UNSW, Sydney
Publications
Conference Papers
XW. Ng, S. Gunasekera, S. Thomas, R. Varcoe, and T. Barber, “Regular Non-Invasive Surveillance of Arteriovenous Fistula (AVF)”, presented at 5th Annual 3D Med Australia Conference, Melbourne, Australia, 2019
O. XW, Ng, “A predictive tool for Arteriovenous Fistula (AVF) Failure”, 2018 UNSW Engineering Postgraduate Symposium, Sydney, Australia, 2018. ISBN: 978-0-9953910-2-4
Z. Khan, O. XW. Ng, V. Timchenko, M. Jackson, “Effects of variation in blood perfusion and anatomy on modelled temperature distribution during wIRA hyperthermia”, 34th Annual Meeting of The Society for Thermal Medicine, Cancún, Mexico, 2017
Current project: AVF Failure Prediction Model
Arteriovenous fistula (AVF) is a necessary surgically modified vascular structure to allow chronic kidney disease (CKD) patients to undergo hemodialysis. Unfortunately, complications often arise in an AVF causing it to not provide adequate blood flow to function normally.
My research focuses on methods to predict arteriovenous fistula (AVF) failure based on the combination of existing patient’s data and computational modelling. I work closely with vascular surgeons in the Prince of Wales Hospital to study the condition of an AVF by scanning patients with an ultrasound imaging system. With patients’ data, I aim to develop an indicator for AVF failure, by simulating close to real world condition of the AVF via computational modelling. This research aims to contribute to the on-going efforts to reduce AVF failure, and to improve the quality of life of CKD patient by effectively aid surgical planning and outcome.