Department of Tuberculosis, Wuxi No.5 People's Hospital, Wuxi 214000, Jiangsu, China.
Department of Emergency, Huzhou Central Hospital, Huzhou 313000, Zhejiang, China.
Comput Intell Neurosci. 2022 May 19;2022:3141807. doi: 10.1155/2022/3141807. eCollection 2022.
The drug resistance and influencing factors of patients with pulmonary tuberculosis were investigated, and a dual attention dilated residual network (DADRN) algorithm was proposed. The algorithm was applied to process and analyze lung computed tomography (CT) images of 400 included patients with pulmonary tuberculosis. Besides, sparse code book algorithm and bag of visual word (BOVW) algorithms were introduced and compared, and the influencing factors of pulmonary tuberculosis drug resistance were analyzed. The results demonstrated that the localization precision of lung consolidation, nodules, and cavities by the DADRN algorithm reached 91.2%, 92.5%, and 93.8%, respectively. The recall rate of the three algorithms amounted to 83.55%, 84.5%, and 86.4%, respectively. Both localization precision and recall rate of the DADRN algorithm were higher than those of other two algorithms ( < 0.05). The drug resistance rate of streptomycin, isoniazid, and rifampin of the patients aged between 40 and 59 was all higher than those of the patients in other age groups. The drug resistance rate of streptomycin, isoniazid, and rifampin of retreated patients was all higher than those of patients initially treated. The drug resistance rate of streptomycin, isoniazid, and rifampin of the patients with tuberculosis contact was all higher than those of the patients without tuberculosis contact ( < 0.05). Based on the above results, the accuracy of CT images processed by dual attention-based dilated residual classification network algorithm was higher than that processed by other two algorithms. Age, medical history, and history of exposure to tuberculosis were the influencing factors of the drug resistance of patients with pulmonary tuberculosis.
研究了肺结核患者的耐药性及其影响因素，并提出了一种双注意扩张残差网络（DADRN）算法。该算法应用于处理和分析 400 例肺结核患者的肺部计算机断层扫描（CT）图像。此外，还引入并比较了稀疏码本算法和词袋视觉（BOVW）算法，并分析了肺结核耐药的影响因素。结果表明，DADRN 算法对肺实变、结节和空洞的定位精度分别达到 91.2%、92.5%和 93.8%。三种算法的召回率分别达到 83.55%、84.5%和 86.4%。DADRN 算法的定位精度和召回率均高于其他两种算法（<0.05）。40-59 岁患者的链霉素、异烟肼和利福平耐药率均高于其他年龄组患者。复治患者的链霉素、异烟肼和利福平耐药率均高于初治患者。有结核病接触史的患者的链霉素、异烟肼和利福平耐药率均高于无结核病接触史的患者（<0.05）。基于上述结果，基于双注意的扩张残差分类网络算法处理的 CT 图像的准确性高于其他两种算法。年龄、病史和结核病接触史是肺结核患者耐药性的影响因素。
