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The CVMM Collaborative Grant has brought together Dr Justin Anh-Kiet Phan, Dr Reza Argha and Professor Jamie Vandenberg to explore the development of a pipeline for machine learning-assisted analysis of ECG images, which may help in the diagnosis of serious heart conditions.

Spotlight on:

Dr Justin Anh-Kiet Phan, PhD Candidate at Victor Chang Cardiac Research Institute / St Vincent’s Hospital Sydney

Dr Reza Argha, Lecturer, Graduate School of Biomedical Engineering, UNSW.

Professor Jamie Vandenberg, Co-Deputy Director, Victor Chang Cardiac Research Institute

Tell us about yourselves...

Justin: I am cardiologist and cardiac electrophysiologist completing a PhD in examining the cardiac arrhythmia profile of patients undergoing cancer treatment. I am supported by a NHMRC PhD scholarship and have been fortunate to have been a recipient of the UNSW CVMM grant.

Reza: I am a cross-disciplinary early career researcher with extensive expertise in machine learning (ML), deep learning (DL), advanced health data analytics, biosignal processing, biomedical image processing, and a unique ability to merge these skills to develop a research program and collaborative links aimed at addressing complex and cross-disciplinary problems through ML-empowered integrative data analytic approaches. My three main research focuses are (i) Using ML/DL methods to process biosignal and biomedical data, (ii) development of unobtrusive human monitoring systems for health-related applications, and (iii) Integrative health data analytics to translational medicine.

Jamie: I am interested in understanding cardiac electrical activity and what makes the heart so robust in the face of the stresses and strains of everyday life as well as a wide range of disease insults. My work has had direct impact on understanding the pro-arrhythmic risk of a wide range of drugs, how gene variants affect heart function and prediction of risk of sudden cardiac arrest. I am intrigued by the power of AI to interpret the ECG and wish to understand how we can use that to both predict risk and better understand how the heart works.

Can you describe the research project funded by the CVMM Theme?

The electrocardiogram (ECG) is a commonly tool to study the heart structure and rhythm. Currently, a high degree of expertise is required to analyse ECGs, and there can be subtle findings that even experts miss. Artificial intelligence is a promising tool for the analysis of ECGs. Our research project involves the development of a pipeline for machine learning-assisted analysis of ECG images, which may help in the diagnosis of serious heart conditions and help understand which patients are at risk of cardiac arrest. We are applying this technique to cancer patient ECGs to better understand their cardiac risk.

How did this funding support your research?

The CVMM funding support has allowed for us to hire a biomedical engineer with experience in machine learning to help develop our pipeline, and has allowed us to pay for access to clinical datasets for machine learning.

Have you watched or read anything good lately?

Justin: Dune series by Frank Herbert, Wheel of Time series by Robert Jordan

Jamie: Best books: James by Percival Everett and Edenglassie by Melissa Lucashenko – retelling of familiar stories but from a very different perspective. Best TV series: Midnight Diner (Netflix), especially Series 1. Wonderful evocation of modern Japan

Reza: Best TV series: Presumed Innocent by Apple TV+.