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; Rischpler, Christoph; Dweck, Marc R.; Glaudemans, Andor W. J. M.; Gimelli, Alessia; Georgoulias, Panagiotis; Gheysens, Olivier; Gaemperli, Oliver; Habib, Gilbert; Hustinx, Roland; et al. (European journal of nuclear medicine and molecular imaging, 2021)
Keywords: 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