COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

ABSTRACT:

Objective

Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD.

Methods

Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (n = 373) were retrospectively included as the training cohort, and subjects from another hospital (n = 226) were used as the external test cohort. According to the PFT results, all subjects were labeled as Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1, 2, 3, 4 or normal. Two DenseNet-201 CNNs were trained using CT images of lung parenchyma and bronchial wall to generate

SUBMITTER: Zhang L 

PROVIDER: S-EPMC10993953 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature