Article (Scientific journals)
Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.
Slart, Riemer H. J. A.; Williams, Michelle C.; Juarez-Orozco, Luis Eduardo et al.
2021In European Journal of Nuclear Medicine and Molecular Imaging
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Keywords :
Cardiovascular; Deep learning; Machine learning; Multimodality imaging; Position paper
Abstract :
[en] 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.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Slart, Riemer H. J. A.
Williams, Michelle C.
Juarez-Orozco, Luis Eduardo
Rischpler, Christoph
Dweck, Marc R.
Glaudemans, Andor W. J. M.
Gimelli, Alessia
Georgoulias, Panagiotis
Gheysens, Olivier
Gaemperli, Oliver
Habib, Gilbert
Hustinx, Roland  ;  Université de Liège - ULiège > Département des sciences cliniques > Médecine nucléaire
Cosyns, Bernard
Verberne, Hein J.
Hyafil, Fabien
Erba, Paola A.
Lubberink, Mark
Slomka, Piotr
Išgum, Ivana
Visvikis, Dimitris
Kolossváry, Márton
Saraste, Antti
More authors (12 more) Less
Language :
English
Title :
Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.
Publication date :
2021
Journal title :
European Journal of Nuclear Medicine and Molecular Imaging
ISSN :
1619-7070
eISSN :
1619-7089
Publisher :
Springer, Germany
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 05 May 2021

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