Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.

Authors

  • Victor A Verpalen
    Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
  • Casper F Coerkamp
    Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
  • José P S Henriques
    Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
  • Ivana Išgum
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • R Nils Planken
    Departments of Radiology and Nuclear Medicine (C.P.S.B., A.J.N., P.v.O., R.N.P.) and Cardiology (S.M.B.), Amsterdam University Medical Centers, Location Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.J.M.W.); Department of Research and Development, Pie Medical Imaging BV, Maastricht, the Netherlands (J.P.A.); and Departments of Cardiology (G.P.B., S.A.J.C.) and Radiology (T.L.), University Medical Center Utrecht, Utrecht, the Netherlands.