Artificial intelligence based ct assessment of bronchiectasis: The copdgene study

Alejandro A. Diaz, Pietro Nardelli, Wei Wang, Ruben San Jose Estepar, Andrew Yen, Seth Kligerman, Diego J. Maselli, Wojciech R. Dolliver, Andrew Tsao, Jose L. Orejas, Stefano Aliberti, Timothy R. Aksamit, Kendra A. Young, Gregory L. Kinney, George R. Washko, Edwin K. Silverman, Raul San Jose Estepar

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Background: CT is the standard method used to assess bronchiectasis. A higher airway-To-Artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent of this metric in CT scans. Purpose: To determine the extent of AARs using an artificial intelligence based chest CT and assess the association of AARs with exacerbations over time. Materials and Methods: In a secondary analysis of ever-smokers from the prospective, observational, multicenter COPDGene study, AARs were quantified using an artificial intelligence tool. The percentage of airways with AAR greater than 1 (a measure of airway dilatation) in each participant on chest CT scans was determined. Pulmonary exacerbations were prospectively determined through biannual follow-up (from July 2009 to September 2021). Multivariable zero-inflated regression models were used to assess the association between the percentage of airways with AAR greater than 1 and the total number of pulmonary exacerbations over follow-up. Covariates included demographics, lung function, and conventional CT parameters. Results: Among 4192 participants (median age, 59 years; IQR, 52 67 years; 1878 men [45%]), 1834 had chronic obstructive pulmonary disease (COPD). During a 10-year follow-up and in adjusted models, the percentage of airways with AARs greater than 1 (quartile 4 vs 1) was associated with a higher total number of exacerbations (risk ratio [RR], 1.08; 95% CI: 1.02, 1.15; P = .01). In participants meeting clinical and imaging criteria of bronchiectasis (ie, clinical manifestations with 3% of AARs 1) versus those who did not, the RR was 1.37 (95% CI: 1.31, 1.43; P 001). Among participants with COPD, the corresponding RRs were 1.10 (95% CI: 1.02, 1.18; P = .02) and 1.32 (95% CI: 1.26, 1.39; P .001), respectively. Conclusion: In ever-smokers with chronic obstructive pulmonary disease, artificial intelligence-based CT measures of bronchiectasis were associated with more exacerbations over time.

Original languageEnglish (US)
Article numbere221109
Issue number1
StatePublished - Apr 2023
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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