Deep Learning-Based Analysis of the Effect of Cardiac Color Ultrasound on Chronic Obstructive Pulmonary Disease under Mask Region

Abstract

This study was developed to analyze the application effect of cardiac ultrasound (CUS) in chronic obstructive pulmonary disease (COPD) patients. A mask region-based convolutional neural network (Mask R-CNN) algorithm was constructed for lung disease detection based on deep learning algorithms, and it was compared with the RetinaNet algorithm for lung disease detection. The results showed that the sensitivity, specificity, accuracy, and running time of the Mask R-CNN algorithm were statistically greater than those of the RetinaNet algorithm ( $P<0.05$ ). A total of 92 cases confirmed with lung diseases by pathology were set as experimental group, including 23 cases of COPD classification for level I, II, III, and IV. In addition, 20 cases of healthy adults were selected as control group. The experimental group was compared with the control group, as the lung function decreased, the right atrium diameter (RAD), right ventricle diameter (RVD), and the right ventricular anterior wall thickness (RVAW) increased, the right ventricular ejection fraction (RVEF) gradually decreased, and the AWT-RV, the interventricular septal thickness (IVST), and right ventricular end-diastole volume (RVEDV) changed greatly in the lung function classification ( $P<0.01$ ). It was concluded that the CUS based on the Mask R-CNN algorithm could show the changes in bronchial lumen volume at all levels and could detect and evaluate the lung function diseases.