人工智能在肺结核影像诊断及耐药性预测中的研究进展

doi:10.19982/j.issn.1000-6621.20240123

摘要/Abstract

摘要：

在全球范围内,结核病是单一传染病致死的主要原因,早期诊断肺结核和识别耐药结核病意义重大,但无创精准诊疗仍受限制。随着医疗大数据的发展,人工智能(artificial intelligence, AI)逐渐应用于肺结核研究。AI从影像中挖掘高通量特征,为无创、可重复评估病灶提供了可能。本文就近年来AI技术在肺结核影像诊断与鉴别诊断、病情监测及耐药性预测方面的研究进展进行综述,以期促进肺结核的AI诊断及耐药性预测技术的临床转化,为精准医疗的实现提供支持。

关键词: 结核,肺, 体层摄影术,X线计算机, 人工智能,诊断, 模型,统计学, 综述文献(主题)

Abstract:

Tuberculosis (TB) is the leading cause of death from a single infectious disease worldwide. Early diagnosis of pulmonary tuberculosis (PTB) and identification of drug resistant tuberculosis are of great significance, but non-invasive and precise diagnosis and treatment are still limited. With the development of medical mega data, artificial intelligence (AI) has been gradually applied to PTB research. AI mining high flux characteristics from image, to provide the possibility of non-invasive and reproducible evaluation of lesions. In this paper, the research progress of AI technology in PTB image diagnosis and differential diagnosis, disease monitoring and drug resistance prediction in recent years is reviewed, with a view to promoting the clinical translation of AI diagnosis and drug resistance prediction technology for PTB, and providing support for the realization of precision medicine.

Key words: Tuberculosis, pulmonary, Tomography, X-ray computed, Artificial intelligence-assisted diagnosis, Models, statistical, Review literature as topic

中图分类号:

R81
R521

李汶翰, 杨静, 李春华. 人工智能在肺结核影像诊断及耐药性预测中的研究进展[J]. 中国防痨杂志, 2024, 46(9): 1098-1103. doi: 10.19982/j.issn.1000-6621.20240123

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. doi: 10.19982/j.issn.1000-6621.20240123

参考文献 52

[1]	World Health Organization. Global tuberculosis report 2023. Geneva: World Health Organization, 2023.
[2]	Lange C, Dheda K, Chesov D, et al. Management of drug-resistant tuberculosis. Lancet, 2019, 394(10202):953-966. doi:10.1016/S0140-6736(19)31882-3. pmid: 31526739
[3]	Cunha L, Rodrigues S, Rosa da Costa AM, et al. Inhalable chitosan microparticles for simultaneous delivery of isoniazid and rifabutin in lung tuberculosis treatment. Drug Dev Ind Pharm, 2019, 45(8): 1313-1220. doi:10.1080/03639045.2019.1608231. pmid: 30990096
[4]	Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine. J Family Med Prim Care, 2019, 8(7): 2328-2331. doi:10.4103/jfmpc.jfmpc_440_19. pmid: 31463251
[5]	Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40. doi:10.1016/j.metabol.2017.01.011.
[6]	陈冲, 陈俊, 夏黎明. 人工智能促进医学影像临床应用与研究. 放射学实践, 2024, 39(1): 12-16. doi:10.13609/j.cnki.1000-0313.2024.01.003.
[7]	吴键, 侯代伦. 深度学习在肺结核影像诊断中的应用. 中国防痨杂志, 2022, 44(1): 91-94. doi:10.19982/j.issn.1000-6621.20210537.
[8]	吴林玉, 许茂盛. 重视人工智能在医学影像中的研究与应用. 中国中西医结合影像学杂志, 2022, 20(4): 307-309. doi:10.3969/j.issn.1672-0512.2022.04.001.
[9]	World Health Organization. WHO consolidated guidelines on tuberculosis: Module 2: screening-systematic screening for tuberculosis disease. Geneva: World Health Organization, 2021.
[10]	Khan FA, Majidulla A, Tavaziva G, et al. Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease. Lancet Digit Health, 2020, 2(11): e573-e581. doi:10.1016/S2589-7500(20)30221-1.
[11]	Yang Y, Xia L, Liu P, et al. A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm. Front Med (Lausanne), 2023, 10: 1195451. doi:10.3389/fmed.2023.1195451.
[12]	Nafisah SI, Muhammad G. Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence. Neural Comput Appl, 2022:1-21. doi:10.1007/s00521-022-07258-6.
[13]	Jasmine Pemeena Priyadarsini M, Kotecha K, Rajini GK, et al. Lung Diseases Detection Using Various Deep Learning Algorithms. J Healthc Eng, 2023, 2023:3563696. doi:10.1155/2023/3563696.
[14]	Mahbub MK, Biswas M, Gaur L, et al. Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis. Inf Sci (N Y), 2022, 592:389-401. doi:10.1016/j.ins.2022.01.062.
[15]	Simi Margarat G, Hemalatha G, Mishra A, et al. Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications. Comput Intell Neurosci, 2022, 2022:3357508. doi:10.1155/2022/3357508.
[16]	Kim K, Lee JH, Je Oh S, et al. AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems. Comput Methods Programs Biomed, 2023, 240: 107643. doi:10.1016/j.cmpb.2023.107643.
[17]	吴树才, 王新举, 纪俊雨, 等. 基于深度学习卷积神经网络的肺结核CT诊断模型效能初探. 中华结核和呼吸杂志, 2021, 44(5): 450-455. doi:10.3760/cma.j.cn112147-20210108-00026.
[18]	Yan C, Wang L, Lin J, et al. A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis. Eur Radiol, 2022, 32(4):2188-2199. doi:10.1007/s00330-021-08365-z.
[19]	刘雪艳, 王芳, 李春华, 等. 基于深度学习的继发性肺结核CT辅助诊断模型构建及验证. 中国防痨杂志, 2024, 46(3): 279-287. doi:10.19982/j.issn.1000-6621.20230356.
[20]	Devasia J, Goswami H, Lakshminarayanan S, et al. Observer Performance Evaluation of a Deep Learning Model for Multilabel Classification of Active Tuberculosis Lung Zone-Wise Manifestations. Cureus, 2023, 15(9):e44954. doi:10.7759/cureus.44954.
[21]	Choi SY, Choi A, Baek SE, et al. Effect of multimodal diagnostic approach using deep learning-based automated detection algorithm for active pulmonary tuberculosis. Sci Rep, 2023, 13(1):19794. doi:10.1038/s41598-023-47146-0.
[22]	Venkataramana L, Prasad DVV, Saraswathi S, et al. Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques. Med Biol Eng Comput, 2022, 60(9):2681-2691. doi:10.1007/s11517-022-02632-x.
[23]	Rajaraman S, Zamzmi G, Folio LR, et al. Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers. Front Genet, 2022, 13:864724. doi:10.3389/fgene.2022.864724.
[24]	Devasia J, Goswami H, Lakshminarayanan S, et al. Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach. Sci Rep, 2023, 13(1):887. doi:10.1038/s41598-023-28079-0. pmid: 36650270
[25]	Lee JH, Park S, Hwang EJ, et al. Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals. Eur Radiol, 2021, 31(2):1069-1080. doi:10.1007/s00330-020-07219-4.
[26]	Kazemzadeh S, Yu J, Jamshy S, et al. Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists. Radiology, 2023, 306(1):124-137. doi:10.1148/radiol.212213.
[27]	Zhou W, Cheng G, Zhang Z, et al. Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing. Quant Imaging Med Surg, 2022, 12(4):2344-2355. doi:10.21037/qims-21-676.
[28]	Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol, 2020, 65(24):24TR02. doi:10.1088/1361-6560/aba798.
[29]	Feng B, Chen X, Chen Y, et al. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas. Eur Radiol, 2020, 30(12):6497-6507. doi:10.1007/s00330-020-07024-z.
[30]	Feng B, Chen X, Chen Y, et al. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. Eur J Radiol, 2020, 128:109022. doi:10.1016/j.ejrad.2020.109022.
[31]	Tan H, Bates JHT, Matthew Kinsey C. Discriminating TB lung nodules from early lung cancers using deep learning. BMC Med Inform Decis Mak, 2022, 22(1):161. doi:10.1186/s12911-022-01904-8.
[32]	韩喜琴, 王敬, 谭秋清, 等. 93例非结核分枝杆菌肺病临床分析. 中国防痨杂志, 2023, 45(4): 355-361. doi:10.19982/j.issn.1000-6621.20220519.
[33]	Park M, Lee Y, Kim S, et al. Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning. BMC Infect Dis, 2023, 23(1):32. doi:10.1186/s12879-023-07996-5.
[34]	Wang L, Ding W, Mo Y, et al. Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework. Eur J Nucl Med Mol Imaging, 2021, 48(13):4293-4306. doi:10.1007/s00259-021-05432-x.
[35]	Ying C, Li X, Lv S, et al. T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis. Int J Infect Dis, 2022, 125:42-50. doi:10.1016/j.ijid.2022.09.031.
[36]	Gao Y, Zhang Y, Hu C, et al. Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning. Front Public Health, 2023, 11:1247141. doi:10.3389/fpubh.2023.1247141.
[37]	Nijiati M, Zhou R, Damaola M, et al. Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis. Front Mol Biosci, 2022, 9:1086047. doi:10.3389/fmolb.2022.1086047.
[38]	Higashiguchi M, Nishioka K, Kimura H, et al. Prediction of the duration needed to achieve culture negativity in patients with active pulmonary tuberculosis using convolutional neural networks and chest radiography. Respir Investig, 2021, 59(4):421-427. doi:10.1016/j.resinv.2021.01.004. pmid: 33707161
[39]	Nijiati M, Guo L, Abulizi A, et al. Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes. Eur J Radiol, 2023, 169:111180. doi:10.1016/j.ejrad.2023.111180.
[40]	王泊宁, 李涛, 陈伟. 耐药结核病经济负担研究进展. 中国防痨杂志, 2023, 45(6): 607-612. doi:10.19982/j.issn.1000-6621.20230018.
[41]	Gao XW, Qian Y. Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques. Mol Pharm, 2018, 15(10):4326-4335. doi:10.1021/acs.molpharmaceut.7b00875.
[42]	Jaeger S, Juarez-Espinosa OH, Candemir S, et al. Detecting drug-resistant tuberculosis in chest radiographs. Int J Comput Assist Radiol Surg, 2018, 13(12):1915-1925. doi:10.1007/s11548-018-1857-9. pmid: 30284153
[43]	Karki M, Kantipudi K, Yu H, et al. Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021:2964-2967. doi:10.1109/EMBC46164.2021.9630189. pmid: 34891867
[44]	Karki M, Kantipudi K, Yang F, et al. Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays. Diagnostics (Basel), 2022, 12(1):188. doi:10.3390/diagnostics12010188.
[45]	Ureta J, Shrestha A. Identifying drug-resistant tuberculosis from chest X-ray images using a simple convolutional neural network. J Phys Conf Ser, 2021, 2071(1): 012001. doi:10.1088/1742-6596/2071/1/012001.
[46]	Tulo SK, Ramu P, Swaminathan R. An Automated Approach to Differentiate Drug Resistant Tuberculosis in Chest X-ray Images Using Projection Profiling and Mediastinal Features. Stud Health Technol Inform, 2021, 281:512-513. doi:10.3233/SHTI210220. pmid: 34042626
[47]	Prasitpuriprecha C, Jantama SS, Preeprem T, et al. Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System. Pharmaceuticals (Basel), 2022, 16(1):13. doi:10.3390/ph16010013.
[48]	Sethanan K, Pitakaso R, Srichok T, et al. Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification. Front Med (Lausanne), 2023, 10:1122222. doi:10.3389/fmed.2023.1122222.
[49]	Nijiati M, Guo L, Tuersun A, et al. Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients. iScience, 2023, 26(11):108326. doi:10.1016/j.isci.2023.108326.
[50]	Wells L, Bednarz T. Explainable AI and Reinforcement Learning-A Systematic Review of Current Approaches and Trends. Front Artif Intell, 2021, 4:550030. doi:10.3389/frai.2021.550030.
[51]	Liang S, Ma J, Wang G, et al. The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Front Med (Lausanne), 2022, 9:935080. doi:10.3389/fmed.2022.935080.
[52]	Acharya V, Dhiman G, Prakasha K, et al. AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model. Comput Intell Neurosci, 2022, 2022: 2399428. doi:10.1155/2022/2399428.