Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.

Journal: The Lancet. Digital health
PMID: 33328058

Abstract

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoracic CT scans are widely used for diagnostic and screening purposes for lung cancer. In this proof-of-concept study, we aimed to evaluate a software pipeline for the automated detection of COPD, based on deep learning and a dataset of low-dose CTs that were performed for early detection of lung cancer.

Authors

  • Lisa Y W Tang
  • Harvey O Coxson
    University of British Columbia Centre for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada.
  • Stephen Lam
    University of British Columbia-British Columbia Cancer Agency and Vancouver General Hospital, Vancouver, British Columbia, Canada.
  • Jonathon Leipsic
    Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
  • Roger C Tam
    Department of Radiology, University of British Columbia, Vancouver, BC, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
  • Don D Sin
    Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada.

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