Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

ABSTRACT: BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convo

SUBMITTER: Bodduluri S 

PROVIDER: S-EPMC7406302 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature