“18F-NaF PET combined with anatomical imaging provided by CT angiography has the potential to enable precision medicine by guiding the use of advanced therapeutic interventions,” noted Slomka. “Our study supports the use of artificial intelligence methods for integrating multimodality imaging and clinical data for robust prediction of heart attacks.”
The authors of “Machine Learning with 18F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction” include Jacek Kwiecinski, Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, and Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland; Evangelos Tzolos, Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, and BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom; Mohammed N. Meah, Alastair J. Moss, Michelle C. Williams, David E. Newby and Marc R. Dweck, BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom; Sebastien Cadet and Daniel S. Berman, Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California; Philip D. Adamson, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand; Kajetan Grodecki and Damini Dey, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; Nikhil V. Joshi, Bristol Heart Institute, University of Bristol, United Kingdom; Edwin J.R. van Beek, BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, and Edinburgh Imaging, Queens Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom; and Piotr J. Slomka, Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
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