Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA.

Journal: Atherosclerosis
Published Date:

Abstract

BACKGROUND AND AIMS: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category.

Authors

  • Giuseppe Muscogiuri
    Clinical Cardiology Unit and Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
  • Mattia Chiesa
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Michela Trotta
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Marco Gatti
    Department of Surgical Sciences, University of Turin, Turin, Italy.
  • Vitanio Palmisano
    Department of Medical Imaging, University of Cagliari, Monserrato, Italy.
  • Serena Dell'Aversana
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Francesca Baessato
    Section of Cardiology, Department of Medicine, University of Verona, Verona, Italy.
  • Annachiara Cavaliere
    Department of Medicine, Institute of Radiology, University of Padova, Padua, Italy.
  • Gloria Cicala
    Section of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy.
  • Antonella Loffreno
    Department of Cardiology, University of Insubria, Varese, Italy.
  • Giulia Rizzon
    Department of Medicine, Institute of Radiology, University of Padova, Padua, Italy.
  • Marco Guglielmo
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Andrea Baggiano
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Laura Fusini
    Department of Cardiovascular Sciences, Centro Cardiologico Monzino, Milan, Italy.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.
  • Daniele Andreini
    Division of Cardiology and Cardiac Imaging, IRCCS Ospedale Galeazzi - Sant'Ambrogio Milan, Italy.
  • Mauro Pepi
    Clinical Cardiology Unit and Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
  • Mark G Rabbat
    Loyola University Medical Center, Maywood, IL, USA.
  • Andrea I Guaricci
    Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital "Policlinico Consorziale" of Bari, Bari, Italy.
  • Carlo N De Cecco
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).
  • Gualtiero Colombo
    School of Computer Science & Informatics, Cardiff University, UK.
  • Gianluca Pontone
    Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.