基于胸部X线卷积神经网络模型诊断儿童不同病原体社区获得性肺炎  

Chest X-ray model based on convolution neural network in diagnosis of children community acquired pneumonia caused by different pathogens

作　　者：胡莎莎 朱永北 董迪[3] 王蓓[1] 王驰 周作福[4] 徐保平[5] 刘秀云[5] 彭芸[1] HU Shasha;ZHU Yongbei;DONG Di;WANG Bei;WANG Chi;ZHOU Zuofu;XU Baoping;LIU Xiuyun;PENG Yun(Imaging Center,National Center for Children'Health,Beijing Children's Hospital,Capital Medical University,Beijing 100045,China;School of Medicine and Engineering,Beihang University,Beijing 100191,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Department of Radiology,Fujian Provincial Maternity and Children's Hospital,Fuzhou 350000,China;Department of Respiratory,National Center for Children'Health,Beijing Children's Hospital,Capital Medical University,Beijing 100045,China)

机构地区：[1]国家儿童医学中心首都医科大学附属北京儿童医院影像中心,北京100045 [2]北京航空航天大学医学科学与工程学院,北京100191 [3]中国科学院自动化研究所,北京100190 [4]福建省妇幼保健院放射科,福建福州350000 [5]国家儿童医学中心首都医科大学附属北京儿童医院呼吸科,北京100045

出　　处：《中国医学影像技术》2021年第8期1163-1167,共5页Chinese Journal of Medical Imaging Technology

基　　金：北京市医院管理局儿科学科协同发展中心专项创新推广项目(XTCX201814);北京市医院管理局儿科学科协同发展中心专项重点项目子课题(XTZD20180104)。

摘　　要：目的评价基于胸部X线卷积神经网络(CNN)模型诊断儿童不同病原体社区获得性肺炎(CAP)的价值。方法纳入1769例CAP患儿,根据病原学诊断分为病毒组(n=487)、细菌组(n=496)及肺炎支原体(MP)组(n=786),对比组间胸部X线征象的差异;将患儿以7∶1∶2比例随机分为训练集、验证集和测试集,对测试集患儿根据性别和年龄分为不同亚组进行分层分析。基于胸部X线片分割全肺和病灶ROI,分别训练全肺模型和局部模型,通过混淆矩阵评估2种模型的整体效能;绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),评价2种模型诊断不同病原CAP的效能;采用Delong检验比较模型诊断效能的差异。结果3组病变累及范围、受累肺组织密度改变特点、肺过度通气及空洞差异均有统计学意义(P均<0.05)。全肺模型及局部模型诊断不同病原CAP的准确率分别为61.85%及58.04%,精确度分别为63.77%及54.05%。全肺模型和局部模型诊断MP性CAP的效能最佳,AUC分别为0.798及0.819;全肺模型诊断病毒及细菌性CAP的AUC均大于局部模型(P均<0.05)。全肺模型和局部模型诊断测试集中男性亚组和女性亚组不同病原CAP的AUC、诊断高年龄亚组和低年龄亚组病毒性及细菌性CAP的AUC差异均无统计学意义(P均>0.05),诊断高年龄亚组和低年龄亚组MP性CAP的AUC差异均有统计学意义(P均<0.05)。结论基于胸部X线片建立CNN模型诊断儿童不同病原体CAP的效能较好;全肺模型优于局部模型,2种模型均对MP性CAP诊断效能最佳。Objective To evaluate the value of chest X-ray model based on convolution neural network(CNN)in diagnosis of children community acquired pneumonia(CAP)caused by different pathogens.Methods A total of 1769 CAP children were enrolled and divided into virus group(n=487),bacteria group(n=496)and mycoplasma pneumoniae(MP)group(n=786)according to the etiological diagnosis.The chest X-ray findings were compared among groups.Then the children were randomly divided into training set,verification set and test set at the ratio of 7∶1∶2.Children in the test set were further divided into subgroups according to gender and age for stratified analysis.Based on chest X-ray segmentation of whole lung and lesion's ROI,the full lung model and local model were trained respectively,and the overall efficacy of these 2 models were evaluated with confusion matrix.Then the receiver operating characteristic(ROC)curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the effectiveness of these 2 models in diagnosis of CAP caused by different pathogens.The Delong test was used to compare the diagnostic efficiency between the models.Results There were statistically differences of lesion involvement scope,characteristic of density change,hyperventilation and cavitation of the lungs among 3 groups(all P<0.05).The accuracy of the full lung model and the local model for diagnosis of CAP caused by different pathogens was 61.85%and 58.04%,and the local model was 63.77%and 54.05%,respectively.Both the full lung model and the local model had the best diagnostic efficacy for MP CAP,and the AUC was 0.798 and 0.819,respectively.The AUC of the full lung model in detecting viral and bacterial CAP were both higher than that of the local model(both P<0.05).There was no significant difference of AUC between models in diagnosing the pathogen of infection in the male subgroup and the female subgroup(all P>0.05),nor of AUC of 2 models for diagnosis of viral and bacterial CAP between the high age subgroup and the low age subgroup(all

关 键 词：肺炎 儿童 X线 放射摄影术 胸部 神经网络 计算机 

分 类 号：R725.6[医药卫生—儿科] R814.41[医药卫生—临床医学]