A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

被引：0
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作者：
Liang Fu
Lei Wang
Haibo Wang
Min Yang
Qianting Yang
Yi Lin
Shanyi Guan
Yongcong Deng
Lei Liu
Qingyun Li
Mengqi He
Peize Zhang
Haibin Chen
Guofang Deng
机构：
[1] National Clinical Research Center for Infectious Disease,Division Two of the Pulmonary Diseases Department, The Third People’s Hospital of Shenzhen
[2] Southern University of Science and Technology,Institute for Hepatology, The Third People’s Hospital of Shenzhen
[3] Breax Laboratory,Medical Examination Department, The Third People’s Hospital of Shenzhen
[4] PCAB Research Center of Breath and Metabolism,Pulmonary Diseases Out
[5] Peking University Clinical Research Institute,Patient Department, The Third People’s Hospital of Shenzhen
[6] Peking University First Hospital,undefined
[7] National Clinical Research Center for Infectious Disease,undefined
[8] Southern University of Science and Technology,undefined
[9] National Clinical Research Center for Infectious Disease,undefined
[10] Southern University of Science and Technology,undefined
[11] National Clinical Research Center for Infectious Disease,undefined
[12] Southern University of Science and Technology,undefined
来源：
BMC Infectious Diseases | / 23卷
关键词：
Pulmonary tuberculosis; Machine learning; Volatile organic compounds; Breathomics;
D O I：
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学科分类号：
摘要：
What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application.What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort.How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies.
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