Application of artificial intelligence in diagnosis of pulmonary tuberculosis

Author:

Du Jingli1,Su Yue1,Qiao Juan1,Gao Shang2,Dong Enjun1,Wang Ruilan1,Nie Yanhui1,Ji Jing1,Wang Zhendong1,Liang Jianqin3,Gong Wenping4

Affiliation:

1. Tuberculosis Control Team, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China

2. Department of Computer Science and Technology, College of Management Science and Engineering, Capital University of Economics and Business, Beijing 100071, China;

3. The Second Ward of Tuberculosis Department, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China

4. Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference12 articles.

1. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning;Li;Mil Med Res,2023

2. A study on the effect of artificial intelligence automatic film reading technology in active tuberculosis screening of HIV/AIDS population (in Chinese);Wang;Chin J Antituberc,2021

3. A preliminary investigation on a deep learning convolutional neural networks based pulmonary tuberculosis CT diagnostic model (in Chinese);Wu;Chin J Tuberc Respir Dis,2021

4. An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases;Griffith;Am J Respir Crit Care Med,2007

5. Deep-learning-aided detection of mycobacteria in pathology specimens increases the sensitivity in early diagnosis of pulmonary tuberculosis compared with bacteriology tests;Zaizen;Diagnostics (Basel),2022

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