Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning.

Journal: Academic radiology
PMID: 39191563

Abstract

RATIONALE AND OBJECTIVES: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features.

Authors

  • Leila Lukhumaidze
    Toronto Metropolitan University, Toronto, ON, Canada (L.L., M.K.).
  • James C Hogg
    Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada (J.C.H., W.C.T.).
  • Jean Bourbeau
    Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada (J.B.); Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada (J.B.).
  • Wan C Tan
    Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada (J.C.H., W.C.T.).
  • Miranda Kirby
    Department of Physics, Toronto Metropolitan University, Toronto, Canada (M.K.).

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