Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.

Journal: PloS one
Published Date:

Abstract

OBJECTIVE: In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population.

Authors

  • John T Murchison
    Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
  • Gillian Ritchie
    Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
  • David Senyszak
    Edinburgh Imaging facility QMRI, University of Edinburgh, Edinburgh, United Kingdom.
  • Jeroen H Nijwening
    Aidence, Amsterdam, The Netherlands.
  • Gerben van Veenendaal
    Aidence, Amsterdam, The Netherlands.
  • Joris Wakkie
    Aidence, Amsterdam, The Netherlands.
  • Edwin J R van Beek
    Department of Radiology, University of Edinburgh, and Edinburgh Imaging, Queen's Medical Research Institute, Edinburgh, Scotland, UK.