Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem / Spadarella, Gaia; Perillo, Teresa; Ugga, Lorenzo; Cuocolo, Renato. - In: CURRENT CARDIOVASCULAR IMAGING REPORTS. - ISSN 1941-9066. - (2022). [10.1007/s12410-022-09563-z]

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Spadarella, Gaia;Perillo, Teresa;Ugga, Lorenzo;Cuocolo, Renato
2022

Abstract

Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.
2022
Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem / Spadarella, Gaia; Perillo, Teresa; Ugga, Lorenzo; Cuocolo, Renato. - In: CURRENT CARDIOVASCULAR IMAGING REPORTS. - ISSN 1941-9066. - (2022). [10.1007/s12410-022-09563-z]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/869348
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