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Artificial Intelligence-Based Quantification of Cardiac Fat

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Artificial Intelligence in Cardiothoracic Imaging

Part of the book series: Contemporary Medical Imaging ((CMI))

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Abstract

Epicardial adipose tissue (EAT) is emerging as a novel source of biomarkers for cardiovascular and metabolic risk. In particular, certain features of EAT such as thickness, volume, and radiodensity have been associated with the presence of cardiovascular diseases and the occurrence of cardiovascular adverse events. Noninvasive imaging modalities such as echocardiography, computed tomography (CT), and magnetic resonance imaging can be used to assess and measure EAT volume and features, each technique having its individual advantages and limitations. Recently, artificial intelligence methods have been applied to EAT imaging, CT in particular, to alleviate potentially cumbersome tasks related to its assessment by human operators and to investigate the prognostic power of EAT-derived features.

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Correspondence to Caterina B. Monti .

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Monti, C.B., Capra, D., Secchi, F., Codari, M., Sardanelli, F. (2022). Artificial Intelligence-Based Quantification of Cardiac Fat. In: De Cecco, C.N., van Assen, M., Leiner, T. (eds) Artificial Intelligence in Cardiothoracic Imaging. Contemporary Medical Imaging. Humana, Cham. https://doi.org/10.1007/978-3-030-92087-6_30

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  • DOI: https://doi.org/10.1007/978-3-030-92087-6_30

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  • Publisher Name: Humana, Cham

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