Department of General Medicine, Affiliated Hospital of Yan'an University, Yan'an, 716000 Shaanxi, China.
Department of Traditional Chinese Medicine, Affiliated Hospital of Yan'an University, Yan'an, 716000 Shaanxi, China.
Comput Math Methods Med. 2022 Apr 25;2022:4147365. doi: 10.1155/2022/4147365. eCollection 2022.
The objective of this study was to adopt the high-resolution computed tomography (HRCT) technology based on the faster-region recurrent convolutional neural network (Faster-RCNN) algorithm to evaluate the lung infection in patients with type 2 diabetes, so as to analyze the application value of imaging features in the assessment of pulmonary disease in type 2 diabetes. In this study, 176 patients with type 2 diabetes were selected as the research objects, and they were divided into different groups based on gender, course of disease, age, glycosylated hemoglobin level (HbA1c), 2 h C peptide (2 h C-P) after meal, fasting C peptide (FC-P), and complications. The research objects were performed with HRCT scan, and the Faster-RCNN algorithm model was built to obtain the imaging features. The relationships between HRCT imaging features and 2 h C-P, FC-P, HbA1c, gender, course of disease, age, and complications were analyzed comprehensively. The results showed that there were no significant differences in HRCT scores between male and female patients, patients of various ages, and patients with different HbA1c contents ( > 0.05). As the course of disease and complications increased, HRCT scores of patients increased obviously ( < 0.05). The HRCT score decreased dramatically with the increase in the contents of 2 h C-P and FC-P after the meal ( < 0.05). In addition, the results of the Spearman rank correlation analysis showed that the course of disease and complications were positively correlated with the HRCT scores, while the 2 h C-P and FC-P levels after meal were negatively correlated with the HRCT scores. The receiver operating curve (ROC) showed that the accuracy, specificity, and sensitivity of HRCT imaging based on Faster-RCNN algorithm were 90.12%, 90.43%, and 83.64%, respectively, in diagnosing lung infection of patients with type 2 diabetes. In summary, the HRCT imaging features based on the Faster-RCNN algorithm can provide effective reference information for the diagnosis and condition assessment of lung infection in patients with type 2 diabetes.
本研究旨在采用基于快速区域递归卷积神经网络（Faster-RCNN）算法的高分辨率计算机断层扫描（HRCT）技术来评估 2 型糖尿病患者的肺部感染，以分析成像特征在 2 型糖尿病肺部疾病评估中的应用价值。在本研究中，选择了 176 例 2 型糖尿病患者作为研究对象，根据性别、病程、年龄、糖化血红蛋白（HbA1c）、餐后 2 小时 C 肽（2 h C-P）、空腹 C 肽（FC-P）和并发症将他们分为不同的组。对研究对象进行 HRCT 扫描，并构建 Faster-RCNN 算法模型以获取成像特征。综合分析 HRCT 成像特征与 2 h C-P、FC-P、HbA1c、性别、病程、年龄和并发症之间的关系。结果表明，男性和女性患者、不同年龄患者和不同 HbA1c 含量（> 0.05）患者之间的 HRCT 评分无显著差异。随着病程和并发症的增加，患者的 HRCT 评分明显升高（< 0.05）。餐后 2 h C-P 和 FC-P 含量增加，HRCT 评分明显下降（< 0.05）。此外，Spearman 秩相关分析结果显示，病程和并发症与 HRCT 评分呈正相关，而餐后 2 h C-P 和 FC-P 水平与 HRCT 评分呈负相关。受试者工作特征曲线（ROC）显示，基于 Faster-RCNN 算法的 HRCT 成像诊断 2 型糖尿病患者肺部感染的准确性、特异性和敏感性分别为 90.12%、90.43%和 83.64%。综上所述，基于 Faster-RCNN 算法的 HRCT 成像可为 2 型糖尿病患者肺部感染的诊断和病情评估提供有效参考信息。
