Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, Hebei, China.
Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, Hebei, China.
Contrast Media Mol Imaging. 2022 Aug 18;2022:5951418. doi: 10.1155/2022/5951418. eCollection 2022.
This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors for COPD. The questionnaire survey was conducted among 980 permanent residents aged ≥ 40 years old. Among them, 84 patients who were diagnosed with COPD and volunteered to participate in the experiment and 25 healthy people were selected as the research subjects, and all of them underwent CT imaging scans. At the same time, an image noise reduction model based on the DRCNN was proposed to process CT images. The results showed that 84 of 980 subjects were diagnosed with COPD, and the overall prevalence of COPD in this epidemiological survey was 8.57%. Multivariate logistic regression model analysis showed that the regression coefficients of COPD with age, family history of COPD, and smoking were 0.557, 0.513, and 0.717, respectively ( < 0.05). The diagnostic sensitivity, specificity, and accuracy of DRCNN-based CT for COPD were greatly superior to those of single CT and the difference was considerable ( < 0.05). In summary, advanced age, family history of COPD, and smoking were independent risk factors for COPD. CT based on the DRCNN model can improve the diagnostic accuracy of simple CT images for COPD and has good performance in the early screening of COPD.
本研究旨在探讨基于深度学习双残差卷积神经网络（DRCNN）模型的计算机断层扫描（CT）图像对慢性阻塞性肺疾病（COPD）的诊断效果及 COPD 的相关危险因素。采用问卷调查法，对 980 名≥40 岁的常住居民进行调查，筛选出 84 例 COPD 患者（自愿参与实验）和 25 名健康人作为研究对象，均行 CT 影像学扫描。同时提出一种基于 DRCNN 的图像降噪模型处理 CT 图像。结果显示：980 例研究对象中，84 例被诊断为 COPD，本次流行病学调查 COPD 总患病率为 8.57%。多因素 logistic 回归模型分析显示，COPD 与年龄、COPD 家族史、吸烟的回归系数分别为 0.557、0.513、0.717（ < 0.05）。基于 DRCNN 的 CT 对 COPD 的诊断灵敏度、特异度、准确度均明显优于单纯 CT，差异有统计学意义（ < 0.05）。综上所述，高龄、COPD 家族史、吸烟是 COPD 的独立危险因素。基于 DRCNN 模型的 CT 可提高单纯 CT 图像对 COPD 的诊断准确率，对 COPD 的早期筛查具有良好的性能。
