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Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Riemer Slart 1, 2 Michelle Williams 3 Luis Eduardo Juarez-Orozco 4, 5 Christoph Rischpler 6 Marc Dweck 3 Andor Glaudemans 5 Alessia Gimelli 7 Panagiotis Georgoulias 8 Olivier Gheysens 9, 10 Oliver Gaemperli 11 Gilbert Habib 12, 13, 14 Roland Hustinx 15 Bernard Cosyns 16 Hein Verberne 17 Fabien Hyafil 18, 19 Paola Erba 20, 5 Mark Lubberink 21, 22 Piotr Slomka 23 Ivana Išgum 17 Dimitris Visvikis 24 Márton Kolossváry 25 Antti Saraste 26, 27 
Abstract : Abstract In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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Submitted on : Thursday, May 12, 2022 - 12:53:06 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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Riemer Slart, Michelle Williams, Luis Eduardo Juarez-Orozco, Christoph Rischpler, Marc Dweck, et al.. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. European Journal of Nuclear Medicine and Molecular Imaging, Springer Verlag (Germany), 2021, 48 (5), pp.1399-1413. ⟨10.1007/s00259-021-05341-z⟩. ⟨hal-03666321⟩

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