Abstract
PURPOSE: During the acute phase of obstructive pulmonary disease (COPD), completing a standard pulmonary function test may be challenging for some patients. The goal of this experiment is to develop a machine learning model that uses chest CT images for automated diagnosis and grading of COPD patients, aiming to enhance diagnostic efficiency and accuracy. PATIENTS AND METHODS: The study retrospectively included 173 COPD patients and 176 healthy controls from December 2017 to June 2023. Deep learning segmentation modules were used to automatically segment the obtained chest CT images for lung parenchyma, airway, pulmonary artery, and vein. Imaging features were extracted from these segmented regions. The most reliable and relevant features were selected using Mann-Whitney U-test with a significant p-value of 0.05 and the least absolute shrinkage and selection operator (LASSO) method. Machine learning models were established through support vector machine (SVM) classifier in the training set and further tested in the internal testing set. Additional tests were performed on an external testing set with 68 individuals. RESULTS: In the machine learning model for COPD diagnosis, the image model achieved AUC values of 0.981 and 0.977 in the training and testing sets, with corresponding accuracies of 0.949 and 0.956 respectively. For COPD severity grading, the image model obtained AUC values of 0.889 and 0.796 in the training and testing sets, along with accuracies of 0.784 and 0.719. CONCLUSION: The machine learning model based on chest CT images can accurately predict lung function, which can assist in the diagnosis and severity grading of COPD.