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

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Abstract

BackgroundCurrently recommended traditional spirometry outputs do not reflect their relative contributions to airflow, and 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 COPD on computed tomography (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 convolutional neural network, Random Forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10>median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (fSAD>20% on parametric response mapping).ResultsAmong 8,980 individuals, neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) than traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71) and FEV1 %predicted (AUC 0.70, 95%CI 0.68-0.71) ), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) than FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1 %predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).ConclusionsStructural phenotypes of COPD can be identified from spirometry using deep learning and machine learning approaches, demonstrating their potential to identify individuals for targeted therapies.