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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

Saraste Antti; Erba Paola A.; Habib Gilbert; Kolossváry Márton; Rischpler Christoph; Gheysens Olivier; Williams Michelle C.; Hyafil Fabien; Georgoulias Panagiotis; Slart Riemer H. J. A.; Slomka Piotr; Juarez-Orozco Luis Eduardo; Gaemperli Oliver; Gimelli Alessia; Cosyns Bernard; Visvikis Dimitris; Lubberink Mark; Išgum Ivana; Dweck Marc R.; Verberne Hein J.; Glaudemans Andor W. J. M.; Hustinx Roland

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

Saraste Antti
Erba Paola A.
Habib Gilbert
Kolossváry Márton
Rischpler Christoph
Gheysens Olivier
Williams Michelle C.
Hyafil Fabien
Georgoulias Panagiotis
Slart Riemer H. J. A.
Slomka Piotr
Juarez-Orozco Luis Eduardo
Gaemperli Oliver
Gimelli Alessia
Cosyns Bernard
Visvikis Dimitris
Lubberink Mark
Išgum Ivana
Dweck Marc R.
Verberne Hein J.
Glaudemans Andor W. J. M.
Hustinx Roland
Katso/Avaa
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SPRINGER
doi:10.1007/s00259-021-05341-z
URI
https://link.springer.com/article/10.1007/s00259-021-05341-z
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021093047965
Tiivistelmä
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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