Detection of chronic obstructive pulmonary disease with deep learning using inspiratory and expiratory chest computed tomography and clinical information

Background: In recent years, more and more patients with chronic obstructive pulmonary disease (COPD) have remained undiagnosed despite having undergone medical examination. This study aimed to develop a convolutional neural network (CNN) model for automatically detecting COPD using double-phase (inspiratory and expiratory) chest computed tomography (CT) images and clinical information.

Methods: A total of 2,047 participants, including never-smokers, ex-smokers, and current smokers, were prospectively recruited from three hospitals. The double-phase CT images and clinical information of each participant were collected for training the proposed CNN model which integrated a sequence of residual feature extracting blocks network (RFEBNet) for extracting CT image features and a fully connected feed-forward network (FCNet) for extracting clinical features. In addition, the RFEBNet utilizing double- or single-phase CT images and the FCNet using clinical information were conducted for comparison.

Results: The proposed CNN model, which utilized double-phase CT images and clinical information, outperformed other models in detecting COPD with an area under the receiver operating characteristic curve (AUC) of 0.930 [95% confidence interval (CI): 0.913-0.951] on an internal test set (n=307). The AUC was higher than the RFEBNet using double-phase CT images (AUC =0.912, 95% CI: 0.891-0.932), single inspiratory CT images (AUC =0.888, 95% CI: 0.863-0.915), single expiratory CT images (AUC =0.897, 95% CI: 0.874-0.925), and FCNet using clinical information (AUC =0.805, 95% CI: 0.777-0.841). The proposed model also achieved the best performance on an external test (n=516) with an AUC of 0.896 (95% CI: 0.871-0.931).

Conclusions: The proposed CNN model using double-phase CT images and clinical information can automatically detect COPD with high accuracy.