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

• Published in: European journal of nuclear medicine and molecular imaging Vol. 48; no. 5; pp. 1399 - 1413
• Main Authors: 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, Cosyns, Bernard, Verberne, Hein J., Hyafil, Fabien, Erba, Paola A., Lubberink, Mark, Slomka, Piotr, Išgum, Ivana, Visvikis, Dimitris, Kolossváry, Márton, Saraste, Antti
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V; Springer Verlag (Germany) [1976-....]; Springer
• Subjects: Algorithms; Angiography; Artificial Intelligence; Bacteriology; Cardiology; Cardiology and cardiovascular system; Cardiovascular; Cardiovascular diseases; Clinical medicine; Computed tomography; Computer applications; Continuous improvement; Deep learning; Emerging diseases; Guidelines; Human health and pathology; Human health sciences; Human performance; Humans; Imaging; Imaging techniques; Infectious diseases; Learning algorithms; Life Sciences; Machine learning; Mathematical models; Medical imaging; Medicine; Medicine & Public Health; Microbiology and Parasitology; Multimodality imaging; Nuclear Medicine; Oncology; Orthopedics; Parasitology; Photon emission; Position paper; Positron emission; Positron emission tomography; Positron Emission Tomography Computed Tomography; Radiologie, médecine & imagerie nucléaire; Radiology; Radiology, nuclear medicine & imaging; Sciences de la santé humaine; Single photon emission computed tomography; Tomography; Tomography, Emission-Computed, Single-Photon; Tomography, X-Ray Computed; Virology; Position paper; Deep learning; Multimodality imaging; Cardiovascular; Machine learning
• ISSN: 1619-7070; 1619-7089; 1619-7089
• DOI: 10.1007/s00259-021-05341-z

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.