摘要
目的:基于胸部CT影像,采用多种深度学习网络构建肺结核与非结核分枝杆菌肺病的二分类模型,并评估这些模型在两种疾病间的鉴别诊断效能。方法:对2019年10月至2022年12月在同济大学附属上海市肺科医院结核科确诊为肺结核和非结核分枝杆菌肺病患者的胸部CT图像进行回顾性分析。纳入研究的数据被分为肺结核组(197例)和非结核分枝杆菌肺病组(212例)。将数据集的80%分配至训练集,20%分配至测试集。采用深度学习网络(ResNeXt、ResNet、Transformer、SENet、ShuffleNet、Swin-transformer和DenseNet算法)构建二分类模型。采用受试者工作特征(ROC)曲线下面积(AUC)、准确率、敏感度和特异度评价模型的分类性能。在测试集中,将最优人工智能模型与3位不同年资的呼吸科(结核亚专业)医生的诊断性能进行比较。结果:ResNeXt模型、ResNet模型、Transformer模型、SENet模型、ShuffleNet模型、Swin-transformer模型、DenseNet模型在训练集中的AUC值分别为0.894、0.839、0.864、0.816、0.841、0.831、0.829;在测试集中的AUC值分别为0.826、0.816、0.817、0.811、0.784、0.771、0.735。在测试集中,最优模型ResNeXt的诊断性能(AUC值为0.826)高于中年资医生(AUC值为0.679)和低年资医生(AUC值为0.663),差异均有统计学意义(Z值分别为2.035和2.242,P值均<0.05)。结论:基于胸部CT影像的深度学习模型是一种快捷简便的无创诊断工具,在鉴别肺结核和非结核分枝杆菌肺病方面展现出了理想的诊断性能。
Objective:To develop and assess the effectiveness of various deep learning networks based on CT imaging for distinguishing nontuberculous mycobacterial lung disease(NTM-LD)from pulmonary tuberculosis(PTB).Methods:A retrospective analysis was performed on chest CT images from patients with PTB and NTM-LD at the Tuberculosis Department of Shanghai Pulmonary Hospital between October 2019 and December 2022.The data were divided into two groups:the PTB group(197 cases)and the NTM-LD group(212 cases).Each group was split into training and testing sets in an 8∶2 ratio.Diagnostic models were developed using several deep learning networks,including ResNeXt,ResNet,Transformer,SENet,ShuffleNet,Swin-Transformer,and DenseNet.The diagnostic performance of the models was assessed based on the area under the curve(AUC),accuracy,sensitivity,and specificity.The performance of the optimal model was then compared with that of three radiologists with varying years of experience,using the testing set.Results:The AUC values of the ResNeXt,ResNet,Transformer,SENet,ShuffleNet,Swin-Transformer,and DenseNet models on the training set were 0.894,0.839,0.864,0.816,0.841,0.831,and 0.829,respectively.On the test set,the AUC values were 0.826,0.816,0.817,0.811,0.784,0.771,and 0.735,respectively.In the test set,the ResNeXt model,with an AUC of 0.826,outperformed both a moderately experienced doctor(AUC:0.679)and a less experienced doctor(AUC:0.663),with Z values of 2.035 and 2.242,respectively(P<0.05).Conclusion:The deep learning network model based on chest CT images is a rapid,simple,and non-invasive diagnostic tool,demonstrating excellent performance in distinguishing NTM-LD from PTB.
作者
李文婷
王丽
方勇
顾瑾
沙巍
Li Wenting;Wang Li;Fang Yong;Gu Jin;Sha Wei(The Center of Clinic and Research Center of Tuberculosis,Shanghai Pulmonary Hospital,Tongji University School of Medicine,Shanghai 200433,China)
出处
《中国防痨杂志》
CAS
CSCD
北大核心
2024年第10期1236-1242,共7页
Chinese Journal of Antituberculosis
基金
上海市加强公共卫生体系建设三年行动计划(2023—2025)重点学科项目(GWVI-11.1-05)。
关键词
分枝杆菌感染
人工智能
体层摄影术
X线计算机
诊断
鉴别
Mycobacterium infections
Artificial intelligence
Tomography,X-ray computed
Diagnosis,differential
