Rationale Predicting increased risk of future healthcare utilization in chronic obstructive
pulmonary disease (COPD) patients is an important goal for improving patient management …

Background Approximately half of adults with chronic obstructive pulmonary disease
(COPD) remain undiagnosed. Chest CT scans are frequently acquired in clinical practice …

Background Identifying individuals at risk of progressing to COPD may allow for initiation of
treatment to potentially slow the progression of the disease or the selection of subgroups for …

Purpose To develop and evaluate a deep learning (DL) approach to extract rich information
from high‐resolution computed tomography (HRCT) of patients with chronic obstructive …

Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the
risk of poor long-term outcomes. Therefore, clinicians should review all available clinical …

Purpose To develop a deep learning–based algorithm to stage the severity of chronic
obstructive pulmonary disease (COPD) through quantification of emphysema and air …

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary
disease (COPD) affect patient health risk assessment, stratification, and management …

Despite being a major cause of morbidity and mortality, chronic obstructive pulmonary
disease (COPD) is frequently undiagnosed. Yet the burden of disease among the …

Rationale and Objectives Acute chronic obstructive pulmonary disease exacerbations
(AECOPD) have a significant negative impact on the quality of life and accelerate …

Background Thoracic computed tomography (CT) scans are widely performed in clinical
practice, often leading to detection of airway or parenchymal abnormalities in asymptomatic …