[1] |
Zhang Peize, Gao Qian, Deng Guofang.
[18F]FDT-PET-CT technology that may bring revolutionary changes to tuberculosis clinical research
[J]. Chinese Journal of Antituberculosis, 2025, 47(3): 262-265.
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[2] |
Zhong Lingshan, Wang Li, Zhang Shuo, Li Nan, Yang Qingyuan, Ding Wenlong, Chen Xingzhi, Huang Chencui, Xing Zhiheng.
A machine learning model based on CT images combined with radiomics and semantic features for diagnosis of nontuberculous mycobacterium lung disease and pulmonary tuberculosis
[J]. Chinese Journal of Antituberculosis, 2024, 46(9): 1042-1049.
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[3] |
Li Wenhan, Yang Jing, Li Chunhua.
Research progress of artificial intelligence in pulmonary tuberculosis imaging diagnosis and drug resistance prediction
[J]. Chinese Journal of Antituberculosis, 2024, 46(9): 1098-1103.
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[4] |
Qin Liyi, Lyu Pingxin, Guo Lin, Qian Lingjun, Xiao Qian, Yang Yang, Shang Yuanyuan, Jia Junnan, Chu Naihui, Liu Yuanming, Li Weimin.
Deep learning to determine the healing status of pulmonary tuberculosis lesions on CT images
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 272-278.
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[5] |
Liu Xueyan, Wang Fang, Li Chunhua, Tang Guangxiao, Zheng Jiaofeng, Wang Huiqiu, Li Yurui, Wang Jia’nan, Shu Weiqiang, Lyu Shengxiu.
Construction and evaluation of a CT-based deep learning model for the auxiliary diagnosis of secondary tuberculosis
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 279-287.
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[6] |
Yi Wanqing, Zheng Xueyi, Zhang Zhuang, Sun Weirong, Yuan Xiaodong.
Comparison of the performance of deep learning models ResNet18 and ResNet50 based on multiphase CT for the diagnosis of renal tuberculosis
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 288-293.
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[7] |
Pan Ben, Liang Changhua, Han Dongming, Cui Junwei, Yao Yangyang, Wei Zhengqi, Zhen Siyu, Wei Hanyu, Yang Xinmiao.
Model construction and validation for predicting active drug-resistant pulmonary tuberculosis using combined CT radiomics and clinical features
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 294-301.
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[8] |
Yao Yangyang, Liang Changhua, Han Dongming, Cui Junwei, Pan Ben, Wang Huihui, Wei Zhengqi, Zhen Siyu, Wei Hanyu.
Differentiation of pulmonary tuberculosis and nontuberculous mycobacterial pulmonary disease based on computed tomography radiomics combined with clinical features
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 302-310.
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[9] |
Gao Shan, Nie Wenjuan, Hou Dailun, Chu Naihui.
Research progress on imaging manifestations of nontuberculosis mycobacterial pulmonary and application of new artificial intelligence technology
[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 362-366.
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[10] |
Li Wenting, Wang Li, Fang Yong, Gu Jin, Sha Wei.
The value of CT-based deep learning models in differentiating nontuberculous mycobacterial lung disease from pulmonary tuberculosis
[J]. Chinese Journal of Antituberculosis, 2024, 46(10): 1236-1242.
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[11] |
Wen Limin, Hou Dailun.
Current status and progress of imaging evaluation methods for detecting pulmonary tuberculosis combined with lung cancer
[J]. Chinese Journal of Antituberculosis, 2023, 45(6): 620-624.
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[12] |
Hu Yujing, Bian Yanzhu.
Application value of 18F-FDG PET/CT in diagnosis and treatment of tuberculosis
[J]. Chinese Journal of Antituberculosis, 2023, 45(3): 318-322.
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[13] |
Yuan Xiaoji, Sun Xiubin, Han Rong, Ni Conghui, Wang Wuzhang, Yu Dexin.
The value of CT radiomics in differentiating the mediastinal lymph node tuberculosis and mediastinal lymph node metastasis of non-small cell lung cancer
[J]. Chinese Journal of Antituberculosis, 2023, 45(10): 949-956.
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[14] |
Li Xiang, Fu Xuwen, Xu Yanling, Gan Wei, Qi Min, Huang Ying.
The value of CT signs combined with peripheral blood eosinophils in differentiating pleuropulmonary paragonimiasis from pleural tuberculosis in children
[J]. Chinese Journal of Antituberculosis, 2023, 45(1): 38-44.
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[15] |
Wei Ganhui, Zhang Jiacheng, Qiu Xiaowei.
Application value of volume CT value inquantifying the activity of pulmonary tuberculosis lesions above 5 mm
[J]. Chinese Journal of Antituberculosis, 2022, 44(9): 934-939.
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