Bronchiectasis is a chronic respiratory disease characterized by permanent bronchiectasis.The incidence and prevalence of bronchiectasis have assumed continuously grows in global. Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.
Study Type
OBSERVATIONAL
Enrollment
730
No intervention
Ruijin Hospital, Medical School of Shanghai Jiaotong University
Shanghai, Shanghai Municipality, China
Correlations of the artificial intelligence-driven scores with manual scores
Correlations and comparisons of the artificial intelligence-driven scores with manual scores by thoracic radiologists on CT scans of bronchiectasis patients.
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Correlation of the artificial intelligence-driven scores 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
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