The year in cardiology: imaging
█ Current opinion
Dudley Pennell1, Victoria Delgado2, Juhani Knuuti3 Pál Maurovich-Horvat4and Jeroen J. Bax2
1Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, National Heart and Lung Institute, Imperial College, London, UK;
2Department of Cardiology, Heart Lung Centre, Leiden University Medical Centre, Leiden, The Netherlands;
3Turku PET Centre, University of Turku, and Turku University Hospital, Turku, Finland; and
4MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
Multimodality imaging and artificial intelligence applied to imaging techniques have been a major interest in this year. The pathophysiological insights that various imaging modalities have provided in numerous clinical scenarios (heart failure, coronary artery disease, and valvular heart disease) influence the way we evaluate and manage patients. Conventional imaging to assess cardiac structure and function is still the first approach to evaluate patients and decide the management. However, advanced echocardiography with strain imaging techniques, tissue characterization with cardiovascular magnetic resonance (CMR), and assessment of biological processes with nuclear imaging techniques have helped to understand that early intervention may be needed in order to prevent or halt the progression of the disease. By applying machine learning techniques to all these imaging modalities, we are able to generate algorithms that can identify certain patterns of disease or risk and develop decisions in a more personalized way. This Year in Cardiology review articles summarize the most relevant studies in the field of imaging published in the last year.
This year, artificial intelligence and machine learning applied to cardiac imaging has been one of the main novelties. Other advances in non-invasive cardiac imaging published in 2019 are summarized in this Year in Cardiology review article (Take home figure).