Optimization of tuberculosis diagnosis based on artificial intelligence strategy_West China Medical Journal

Authors：

JIAO Lin , HU Xuejiao ,  YING Binwu

Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

YING Binwu, Email: binwuying@126.com

Keywords：

Artificial intelligence; Machine learning; Deep learning; Tuberculosis; Diagnosis

DOI：

10.7507/1002-0179.201807067

Video：

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Tuberculosis is one of the major infectious diseases that seriously endanger human health. Since 2014, it has surpassed human immunodeficiency virus/acquired immunodeficiency syndrome as the first infectious disease in patients with single pathogens. China is the third-largest country in the world in terms of high burden of tuberculosis. In 2016, there were about 900 000 new cases of tuberculosis in China. China is facing a severe tuberculosis epidemic, especially for the early diagnosis of tuberculosis and misdiagnosis of tuberculosis, which leads to delay in treatment and the spread of tuberculosis. With the application of artificial intelligence in the medical field, machine learning and deep learning methods have shown important value in the diagnosis of tuberculosis. This article will explain the application status and future development of machine learning and deep learning in the diagnosis of tuberculosis.

Citation： JIAO Lin, HU Xuejiao, YING Binwu. Optimization of tuberculosis diagnosis based on artificial intelligence strategy. West China Medical Journal, 2018, 33(8): 935-938. doi: 10.7507/1002-0179.201807067 Copy

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8.	Wilkinson RJ, Rohlwink U, Misra UK, et al. Tuberculous meningitis. Nat Rev Neurol, 2017, 13(10): 581-598.
9.	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
10.	Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016, 316(22): 2402-2410.
11.	Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 2017, 284(2): 574-582.
12.	Becker AS, Blüthgen C, Phi Van VD, et al. Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: feasibility study. Int J Tuberc Lung Dis, 2018, 22(3): 328-335.
13.	Gao XW, Qian Y. Prediction of multidrug-resistant TB from ct pulmonary images based on deep learning techniques. Mol Pharm, 2018.
14.	Zak DE, Penn-Nicholson A, Scriba TJ, et al. A blood RNA signature for tuberculosis disease risk: a prospective cohort study. Lancet, 2016, 387(135): 2312-2322.
15.	Berry MP, Graham CM, Mcnab FW, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature, 2010, 466(739): 973-977.
16.	Yang Y, Niehaus KE, Walker TM, et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, 2018, 34(10): 1666-1671.
17.	Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res, 2018, 24(6): 1248-1259.

1. World Health Organization. Global tuberculosis report 2017: WHO/HTM/TB/2017.23. Geneva: World Health Organization, 2017. http://www.who.int/tb/publications/global_report/en/.
2. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
3. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
5. Agranoff D, Fernandez-Reyes D, Papadopoulos MC, et al. Identification of diagnostic marers for tuberculosis by proteomic fingerprinting of serum. Lancet, 2006, 368(9540): 1012-1021.
6. 《中国防痨杂志》编辑委员会, 中国医疗保健国际交流促进会结核病防治分会基础学组和临床学组. 现阶段结核抗体检测在我国临床应用的专家共识. 中国防痨杂志, 2018, 40(1): 9-13.
7. 全国第五次结核病抽样调查技术指导组. 2010 年全国第五次结核病流行病学抽样调查报告. 中国防痨杂志, 2012, 34(8): 485-508.
8. Wilkinson RJ, Rohlwink U, Misra UK, et al. Tuberculous meningitis. Nat Rev Neurol, 2017, 13(10): 581-598.
9. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
10. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016, 316(22): 2402-2410.
11. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 2017, 284(2): 574-582.
12. Becker AS, Blüthgen C, Phi Van VD, et al. Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: feasibility study. Int J Tuberc Lung Dis, 2018, 22(3): 328-335.
13. Gao XW, Qian Y. Prediction of multidrug-resistant TB from ct pulmonary images based on deep learning techniques. Mol Pharm, 2018.
14. Zak DE, Penn-Nicholson A, Scriba TJ, et al. A blood RNA signature for tuberculosis disease risk: a prospective cohort study. Lancet, 2016, 387(135): 2312-2322.
15. Berry MP, Graham CM, Mcnab FW, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature, 2010, 466(739): 973-977.
16. Yang Y, Niehaus KE, Walker TM, et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, 2018, 34(10): 1666-1671.
17. Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res, 2018, 24(6): 1248-1259.

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