Phi Anh Phan
My background is biomedical engineering. I hold a BE in Mechatronics and a Phd in Artificial Intelligence and Control System. My interests include medical devices, robotics, and engineering systems that require integration of hardware, software and modeling.
I am currently developing a medical device technology, called InspiWave, to non-invasively monitor lung function and pulmonary blood flow, and am working towards spinning out the technology from the University. As part of this effort, we have been selected finalist twice in the OneStart life-science business plan competition - one of the largest of its kind. I am also an Enterprise Fellow of the Royal Academy of Engineering.
InspiWave works by delivering small doses of tracer gases into a patient's inhaled breaths and measuring the responses in the exhaled breaths. The collected data is then fed through a mathematical model of the lung and recirculation to estimate the patient's physiological variables. Potential applications include bedside monitoring to reduce ventilator-induced lung injury in intensive care patients, and early diagnosis of chronic-obstructive pulmonary disease in outpatients.
Before joining the NDCN, I worked on the development of 2 respiratory medical devices and researched in self-adaptive AI control algorithms for robots, in Australia.
I am interested in consultancy, drafting new patents, spinouts, and participating in new ventures.
A tidal lung simulation to quantify lung heterogeneity with the Inspired Sinewave Test.
Tran MC. et al, (2020), Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2438 - 2441
Noninvasive cardiac output monitoring in a porcine model using the inspired sinewave technique: a proof-of-concept study.
Bruce RM. et al, (2019), Br J Anaesth, 123, 126 - 134
Method and apparatus for measurement of cardiopulmonary function
PHAN PHI. et al, (2019)
The inspired sine-wave technique: A novel method to measure lung volume and ventilatory heterogeneity.
Bruce RM. et al, (2018), Exp Physiol, 103, 738 - 747
Modelling mixing within the dead space of the lung improves predictions of functional residual capacity.
Harrison CD. et al, (2017), Respir Physiol Neurobiol, 242, 12 - 18