Lung structural abnormalities are complex, time-consuming, and may lack reproducibility to evaluate visually on CT scans. The study's aim is to perform automated recognition of structural abnormalities in CT scans of patients with chronic lung diseases by using dedicated software.
Three chronic lung diseases will constitute the target of the study, by using retrospective data from each lung disease: * Cystic fibrosis * Asthma and COPD * Interstitial lung diseases Dedicated algorithms will be developped for each disease condition.
Study Type
OBSERVATIONAL
Enrollment
800
Hopital Haut Leveque
Pessac, France
Validity of automated measurement
Correlations and comparisons with other biomarker of the disease severity
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Correlation with pulmonary function test
Correlation of quantitative measurement with pulmonary function
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Longitudinal variation over time
Comparison of quantitative measurement at two time points
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Reproducibility
Evaluation of measurements when performed twice
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
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