Sotirios A Tsaftaris, PhD

Chancellor's Fellow and Reader @ University of Edinburgh

Canon Medical/Royal Academy of Engineering Senior Research Fellow in Healthcare AI

Funder: RAENG (UK) Number: RCSRF1819\8\25 Dates: 01 April 2019 - 31 March 2024 Status: Completed


In the UK alone, currently 7 million people live with cardiovascular disease and this number will increase as the population ages. Under-resourced and under-staffed healthcare systems are struggling with the rising caseload and the large volumes of information being generated. Currently, excitement in Artificial Intelligence (AI) for healthcare is high, because of its potential to help stem this information overload and reduce healthcare costs.

The AI paradigm fuelling this excitement heavily depends on well-curated training data and is largely seen as a ‘black box’. In contrast we will:
(1) learn from complex, multimodal, healthcare records with minimal supervision; and
(2) focus on problems underpinning learning data representations optimised to provide a transparent base for the desired diagnoses and predictions. We will then translate these techniques to automated estimation of cardiac biomarkers, disease diagnosis, and most ambitiously, cardiac episode prediction, thus opening roads to preventive care.

People involved and collaborators


  1. A. Chartsias, T. Joyce, G. Papanastasiou, S. Semple, M. Williams, D. Newby, R. Dharmakumar, S.A. Tsaftaris, “Factorised Representation Learning in Cardiac Image Analysis,” submitted, [preprint].


Generously supported by Canon Medical Research Europe, the Royal Academy of Engineering and the School of Egnineering.


News & Progress

Policy event: Intervention at Future-proofing society – how digital health and social care can empower and transform lives, Scottish Parliament and IET Event.
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