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

Abstract

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.

Keywords: Chronic obstructive pulmonary disease (COPD); convolutional neural network (CNN); double-phase; expiratory; inspiratory.

Figure 1

Flow diagram of participants inclusion. CT, computed tomography; PFT, pulmonary function test.

Figure 2

The proposed network architecture for COPD classification. The architecture includes three parts, i.e., a sequence of RFEBNet, a FCNet, and a fusion classifier. RFEBNet is used as an image feature extractor. FCNet is used as a text information extractor. Extracted features from images and clinical information are fed into the fusion classifier for determining COPD or Non-COPD. CT, computed tomography; RFEBNet, residual feature extracting blocks network; FC, fully connected; COPD, chronic obstructive pulmonary disease; CCQ, clinical COPD questionnaire; CAT, COPD assessment test; FCNet, fully connected feed-forward network; ReLU, rectified linear unit.

Figure 3

ROC curve comparison between different models in the internal (A) and external (B) tests, respectively. Model I + E: a sequence of residual feature extracting blocks network using double-phase chest CT images as inputs; Model E: a sequence of residual feature extracting blocks network using expiratory chest CT image as input; Model I: a sequence of residual feature extracting blocks network using inspiratory chest CT image as input; Model C: a fully connected neural network using clinical information as input. ROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve; CI, confidence interval; CT, computed tomography.

Figure 4

Detection of COPD by the proposed model in the internal test set. (A) The predicted probabilities represent the predicted probability that the proposed model assigns to the outcome of COPD. The observed proportions reflect the actual proportion of participants within each decile who were diagnosed with COPD. Reference lines illustrate perfect correlation, with a slope of 1 and an intercept of 0. (B) The Hosmer-Lemeshow test assessed as calibration quality. A nonsignificant P value (>0.05) suggests that there is no evidence for poor calibration. COPD, chronic obstructive pulmonary disease.

Figure 5

Feature visualizations of proposed model for detecting COPD using CAMs technique. The columns from left to right are the original inspiratory CT image, a class activation map, and a class activation map overlaying the original image. (A-D) COPD cases. (E,F) Non-COPD cases. COPD, chronic obstructive pulmonary disease; CAMs, class activation maps; CT, computed tomography.