Imperial College London


Faculty of MedicineInstitute of Clinical Sciences

Reader in Imaging Sciences



+44 (0)20 3313 1510declan.oregan




Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus






BibTex format

author = {O'Regan, DP},
doi = {10.1016/j.crad.2019.04.008},
journal = {Clinical Radiology},
pages = {33--37},
title = {Putting machine learning into motion: applications in cardiovascular imaging},
url = {},
volume = {75},
year = {2020}

RIS format (EndNote, RefMan)

AB - Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that assessing flow and motion is essential for understanding and quantifying normal physiology and disease processes. Conventional image interpretation relies on manual analysis but this often fails to capture important prognostic features in the complex disturbances of cardiovascular physiology. Machine learning (ML) in cardiovascular imaging promises to be a transformative tool and addresses an unmet need for patient-specific management, accurate prediction of future events, and the discovery of tractable molecular mechanisms of disease. This review discusses the potential of ML across every aspect of image analysis including efficient acquisition, segmentation and motion tracking, disease classification, prediction tasks and modelling of genotype–phenotype interactions; however, significant challenges remain in access to high-quality data at scale, robust validation, and clinical interpretability.
AU - O'Regan,DP
DO - 10.1016/j.crad.2019.04.008
EP - 37
PY - 2020///
SN - 0009-9260
SP - 33
TI - Putting machine learning into motion: applications in cardiovascular imaging
T2 - Clinical Radiology
UR -
UR -
VL - 75
ER -