Rina Ariga

My research interest is in integrating data from mulitple modalities: electrocardiograms (ECG), imaging, blood analysis and health records, to develop artificial intelligent tools for early detection of cardiovascular diseases such as heart failure.

My doctoral research pioneered a novel imaging method for patients with hypertrophic cardiomyopathy to measure heart muscle disarray – a microstructural abnormality previously only seen at post mortem, which is believed to be the focus for fatal heart rhythms. I have received several prizes and awards for this work, and the findings were reported in the media.

I have also demonstrated, in collaboration with Oxford’s Computational Cardiovascular Science Team, a machine learning approach to the ECG to detect subtle electrical changes indicative of such structural defects, and the risk of abnormal rhythms. We used computational modelling and simulation of the heart based on magnetic resonance imaging to reveal distinct mechanisms for specific electrical patterns, which may pave the way towards individualised patient treatment.