Chinese Journal of Antituberculosis ›› 2024, Vol. 46 ›› Issue (3): 253-259.doi: 10.19982/j.issn.1000-6621.20240013
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Received:
2024-01-10
Online:
2024-03-10
Published:
2024-03-05
CLC Number:
Li Duo, Lyu Pingxin. Promoting the development and application of artificial intelligence technology in the field of pulmonary tuberculosis imaging[J]. Chinese Journal of Antituberculosis, 2024, 46(3): 253-259. doi: 10.19982/j.issn.1000-6621.20240013
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[1] | 刘再毅, 石镇维. 医学影像人工智能: 进展和未来. 国际医学放射学杂志, 2023, 46(1): 1-4. |
[2] | Zhan Y, Wang Y, Zhang W, et al. Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis. J Clin Med, 2022, 12(1): 303. doi:10.3390/jcm12010303. |
[3] | World Health Organization. WHO consolidated guidelines on tuberculosis: module 2: screening: systematic screening for tuberculosis disease. Geneva: World Health Organization, 2021. |
[4] | Jaeger S, Karargyris A, Candemir S, et al. Automatic screening for tuberculosis in chest radiographs: a survey. Quant Imaging Med Surg, 2013, 3(2): 89-99. doi:10.3978/j.issn.2223-4292.2013.04.03. |
[5] |
Santosh KC, Allu S, Rajaraman S, et al. Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review. J Med Syst, 2022, 46(11): 82. doi:10.1007/s10916-022-01870-8.
pmid: 36241922 |
[6] | Qin ZZ, Ahmed S, Sarker MS, et al. Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health, 2021, 3(9): e543-e554. doi:10.1016/S2589-7500(21)00116-3. |
[7] | 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. |
[8] | Park J, Hwang EJ, Lee JH, et al. Identification of Active Pulmonary Tuberculosis Among Patients With Positive Interferon-Gamma Release Assay Results: Value of a Deep Learning-based Computer-aided Detection System in Different Scenarios of Implementation. J Thorac Imaging, 2023, 38(3): 145-153. doi:10.1097/RTI.0000000000000691. |
[9] | 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. |
[10] | Nijiati M, Ma J, Hu C, et al. Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study. Front Mol Biosci, 2022, 9: 874475. doi:10.3389/fmolb.2022.874475. |
[11] | Ma L, Wang Y, Guo L, et al. Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning. J Xray Sci Technol, 2020, 28(5): 939-951. doi:10.3233/XST-200662. |
[12] | Han D, Chen Y, Li X, et al. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia. Radiol Med, 2023, 128(1): 68-80. doi:10.1007/s11547-022-01580-8. |
[13] | 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. |
[14] | 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. |
[15] |
Yan Q, Wang W, Zhao W, et al. Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics. BMC Pulm Med, 2022, 22(1): 4. doi:10.1186/s12890-021-01766-2.
pmid: 34991543 |
[16] | 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. |
[17] | Liu CJ, Tsai CC, Kuo LC, et al. A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study. Insights Imaging, 2023, 14(1): 67. doi:10.1186/s13244-023-01395-9. |
[18] | Ying C, Li X, Lv S, et al. T-SPOT with CT image analysis based on deep learning for early differential diagnosis of non-tuberculous mycobacteria pulmonary disease and pulmonary tuberculosis. Int J Infect Dis, 2022, 125: 42-50. doi:10.1016/j.ijid.2022.09.031. |
[19] | 姚阳阳, 梁长华, 韩东明, 等. 基于CT影像组学结合临床特征鉴别肺结核与非结核分枝杆菌肺病的研究. 中国防痨杂志, 2024, 46(3):302-310. doi:10.19982/j.issn.1000-6621.20230337. |
[20] | Zhang J, Hao L, Qi M, et al. Radiomics nomogram for preope-rative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules. BMC Cancer, 2023, 23(1): 261. doi:10.1186/s12885-023-10734-4. |
[21] | Zhuo Y, Zhan Y, Zhang Z, et al. Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule. Front Oncol, 2021, 11: 701598. doi:10.3389/fonc.2021.701598. |
[22] | 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. |
[23] | 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. |
[24] | Li X, Zhou Y, Du P, et al. A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis. Applied Intelligence, 2021, 51(6): 4082-4093. doi:10.1007/s10489-020-02051-1. |
[25] | 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. |
[26] | Lee S, Yim JJ, Kwak N, et al. Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs. Radiology, 2021, 301(2): 435-442. doi:10.1148/radiol.2021210063. |
[27] | 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. |
[28] | 秦李祎, 吕平欣, 郭琳, 等. 基于CT图像的肺结核病灶治愈状态判定深度学习模型的建立. 中国防痨杂志, 2024, 46(3):272-278. doi:10.19982/j.issn.1000-6621.20230457. |
[29] |
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 |
[30] |
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 |
[31] |
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 |
[32] | 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. |
[33] | Li Y, Wang B, Wen L, et al. Machine learning and radiomics for the prediction of multidrug resistance in cavitary pulmonary tuberculosis: a multicentre study. Eur Radiol, 2023, 33(1): 391-400. doi:10.1007/s00330-022-08997-9. |
[34] | 潘犇, 梁长华, 韩东明, 等. CT影像组学结合临床特征预测活动性耐药肺结核的模型构建与验证. 中国防痨杂志, 2024, 46(3):294-301. doi:10.19982/j.issn.1000-6621.20230278. |
[35] | Yanagawa M, Ito R, Nozaki T, et al. New trend in artificial intelligence-based assistive technology for thoracic imaging. Radiol Med, 2023, 128(10): 1236-1249. doi:10.1007/s11547-023-01691-w. |
[36] | Rather IH, Kumar S. Generative adversarial network based synthetic data training model for lightweight convolutional neural networks. Multimed Tools Appl, 2023[2023-05-20]. doi:10.1007/s11042-023-15747-6. Online ahead of print. |
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