Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study.

ABSTRACT: Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Bas

SUBMITTER: Oh AS 

PROVIDER: S-EPMC9434819 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature