Imperial College London


Faculty of MedicineInstitute of Clinical Sciences

Professor of Imaging Sciences



+44 (0)20 3313 1510declan.oregan




Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus





Prof Declan O'Regan is an MRC Investigator and Consultant Radiologist who leads the Computational Cardiac Imaging Group at the MRC London Institute of Medical Sciences. He is also Director for Imaging Research at Imperial College Healthcare NHS Trust. He is committed to science engagement and is a past Roentgen (UK) and Rowan-Williams (Australasia) travelling professor lecturing internationally on the role of artificial intelligence (AI) in healthcare. He is board member for the British Society of Cardiac MR and sits on the Advisory Council of the British Heart Foundation. He is also an Associate Editor for npg Digital Medicine and European Heart Journal - Cardiovascular Imaging.

His research is focussed on using machine learning to discover mechanisms that underpin common cardiovascular diseases by integrating data from human imaging, genetics and environmental risk factors. His work includes developing algorithms for predicting human survival from cardiac motion and understanding how complex traits influence the risk of heart failure. He also collaborates with industry to accelerate progress in drug discovery using automated genotype-phenotype modelling.  

Lab website:

Image of the heart created with Siemens Healthineers Cinematic Rendering Technology (O'Regan/Imperial College London).

Credit: O'Regan/Siemens Healthineers

Selected Publications

Journal Articles

Shah M, Inacio M, Lu C, et al., 2023, Environmental and genetic predictors of human cardiovascular ageing, Nature Communications, Vol:14, ISSN:2041-1723, Pages:1-15

Thanaj M, Mielke J, McGurk K, et al., 2022, Genetic and environmental determinants of diastolic heart function, Nature Cardiovascular Research, Vol:1, ISSN:2731-0590, Pages:361-371

Simoes Monteiro de Marvao A, McGurk K, Zheng S, et al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, Vol:78, ISSN:0735-1097, Pages:1097-1110

Bai W, Suzuki H, Huang J, et al., 2020, A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine, Vol:26, ISSN:1078-8956, Pages:1654-1662

Meyer H, Dawes T, Serrani M, et al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol:584, ISSN:0028-0836, Pages:589-594

Bello G, Dawes T, Duan J, et al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol:1, ISSN:2522-5839, Pages:95-104

Biffi C, Simoes Monteiro de Marvao A, Attard M, et al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework, Bioinformatics, ISSN:1367-4803

Schafer S, de Marvao A, Adami E, et al., 2017, Titin-truncating variants affect heart function in disease cohorts and the general population, Nature Genetics, Vol:49, ISSN:1546-1718, Pages:46-53

More Publications