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From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

by 1,2,3,* and 2,4
1
Imperial College Healthcare NHS Trust, London W12 0HS, UK
2
Imperial College London, London SW7 2AZ, UK
3
Cambridge University Hospital, Cambridge CB2 0QQ, UK
4
National Heart & Lung Institute, Imperial College London, London SW3 6LY, UK
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(12), 1004; https://doi.org/10.3390/diagnostics10121004
Received: 24 October 2020 / Revised: 19 November 2020 / Accepted: 24 November 2020 / Published: 25 November 2020
(This article belongs to the Special Issue Pulmonary Hypertension: Diagnosis and Management)
Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure–function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come. View Full-Text
Keywords: pulmonary vascular morphometrics; pulmonary vascular imaging; pulmonary perfusion imaging; blood flow imaging; AI and pulmonary vasculature; machine learning and pulmonary circulation; deep learning and pulmonary circulation; radiomics pulmonary vascular morphometrics; pulmonary vascular imaging; pulmonary perfusion imaging; blood flow imaging; AI and pulmonary vasculature; machine learning and pulmonary circulation; deep learning and pulmonary circulation; radiomics
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MDPI and ACS Style

Gopalan, D.; Gibbs, J.S.R. From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation? Diagnostics 2020, 10, 1004. https://doi.org/10.3390/diagnostics10121004

AMA Style

Gopalan D, Gibbs JSR. From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation? Diagnostics. 2020; 10(12):1004. https://doi.org/10.3390/diagnostics10121004

Chicago/Turabian Style

Gopalan, Deepa; Gibbs, J. S.R. 2020. "From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?" Diagnostics 10, no. 12: 1004. https://doi.org/10.3390/diagnostics10121004

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