Advancing AI-ECG Diagnosis Using Deep Learning and Neural Architecture Search

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Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of AI models to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a data set of 2.3 million ECGs collected from patients with 7 years follow-up, we developed a DNN model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. Our model demonstrated cardiologist-level accuracy in interpretable cardiac diagnosis, and the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available. In addition, we explored the design of optimal DNN models through of a novel Neural Architecture Search (NAS) approach, which was able to find networks outperformed the state-of-the-art models with fewer than 5% parameters.

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Dr. Lei Lu is a Lecturer in Health Data Science and AI at King’s College London, and a Visiting Research Fellow at the Institute of Biomedical Engineering, University of Oxford. Lei obtained his PhD from the Harbin Institute of Technology in China, complemented by two-year visiting research at the University of British Columbia in Canada. Upon completing his PhD study, Lei had his postdoctoral research at the University of Melbourne in Australia. Subsequently, he joined the Institute of Biomedical Engineering at University of Oxford as a Senior Research Associate. Lei’s work focuses on clinical machine learning to advance healthcare outcomes. Lei is actively engaged in a range of academic roles, including invited speaker at the IET Annual Healthcare Lecture and the IEEE-EMBS Symposium on MDBS. He also served as conference session chair, workshop committee, and guest editor for IJCAI, CIKM, ICRA, and IEEE JBHI. He received the IET J.A. Lodge Award in 2021, which is presented annually to one early-career researcher with distinction.