Key result
Young Investigator Award recognizes Dr. Arunashis Sau for machine learning research identifying novel ECG risk markers.
Why the study?
The application of machine learning to identify novel risk markers from the ECG beyond conventional clinician interpretation was hypothesized but not established.
Key points are not available for this paper at this time.
Design
Abstract submission and presentation competition with expert review and judging at BCS annual conference
The British Cardiovascular Society (BCS)/British Heart Foundation (BHF)/British Atherosclerosis Society/British Society for Cardiovascular Research Young Investigator Award was established in 2001 to recognise excellence in young researchers intending to pursue a career in cardiovascular clinical medicine or research. It is open to both young clinicians and basic scientists. Clinicians should not have attained consultant status at the time the research was performed and basic scientists should be no more than 5 years post-PhD. Investigators submit an abstract, limited to 1000 words, which is then reviewed, and five finalists are selected to give a 10-minute presentation during the BCS annual conference in Manchester, UK. This is followed by five minutes of questions by the judges. The profiles and presentation summaries of this year’s winner, Dr Sau, and the other finalists are shown below. ![Graphic][1] Dr Arunashis Sau is a clinical research fellow, cardiology registrar and PhD candidate at Imperial College London. His main research interest is the application of machine learning (ML) to further the field of cardiology, including applying deep learning to the surface ECG and to intracardiac electrograms. He studied medicine at Imperial College London, where he was awarded a First Class (Honours) degree in Medical Sciences with Cardiovascular Sciences. His postgraduate clinical training to date has been in London, most recently as a National Institute for Health and Care Research (NIHR) Academic Clinical Fellow. He has been awarded a BHF Clinical Research Training Fellowship and started this in October 2021 under the primary supervision of Dr Fu Siong Ng. Dr Sau’s Young Investigator Award presentation began with the hypothesis that ML has the potential to identify novel markers of risk from the ECG that can go beyond clinician ECG interpretation. In the course of supervised ML training, many thousands of ECG features are identified which are not limited to conventional … [1]: /embed/inline-graphic-1.gif
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Sau et al. (2023) studied this question. The 2023 Young Investigator Award was presented to Dr. Arunashis Sau for his research on applying machine learning to identify novel risk markers from electrocardiograms.
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