胸部CT联合人工智能辅助检测在肺结核诊疗中的研究进展

doi:10.19983/j.issn.2096-8493.20250134

结核与肺部疾病杂志 ›› 2026, Vol. 7 ›› Issue (1): 112-119.doi: 10.19983/j.issn.2096-8493.20250134

胸部CT联合人工智能辅助检测在肺结核诊疗中的研究进展

张嘉诚¹^,², 唐神结³, 侯代伦¹^,²(), 李亮⁴()

¹ 首都医科大学附属北京胸科医院放射科,北京 101149
² 北京市结核病胸部肿瘤研究所放射科,北京 101149
³ 中国疾病预防控制中心结核病防治临床中心办公室,北京 102206
⁴ 北京市结核病胸部肿瘤研究所院办公室,北京 101149

收稿日期:2025-08-24 出版日期:2026-02-20 发布日期:2026-02-09
通信作者: 侯代伦,Email:hodelen@126.com;李亮,Email:liliang69@hotmail.com
基金资助:
国家自然科学基金(82471938);公共卫生技术人才建设项目(学科带头人-03-07)

Advances in research on chest CT combined with artificial intelligence-assisted detection in the diagnosis and treatment of pulmonary tuberculosis

Zhang Jiacheng¹^,², Tang Shenjie³, Hou Dailun¹^,²(), Li Liang⁴()

¹ Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
² Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China
³ Office of Clinical Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
⁴ Hospital Office, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China

Received:2025-08-24 Online:2026-02-20 Published:2026-02-09
Contact: Hou Dailun, Email: hodelen@126.com;Li Liang, Email: liliang69@hotmail.com
Supported by:
National Natural Science Foundation of China(82471938);Public Health Technology Talent Development Project(Academic Leader-03-07)

摘要/Abstract

摘要：

在肺结核(pulmonary tuberculosis,PTB)防控工作中,早期精准诊断与耐药识别具有至关重要的作用。传统实验室检查存在明显滞后性,胸部影像学人工判读过程易受主观因素干扰,而人工智能(artificial intelligence,AI)技术为PTB诊疗流程的优化提供了新方向。目前,基于胸部X线的人工智能辅助检测(chest x-ray computer-aided detection,CXR-CAD)软件已应用于临床日常工作,基于胸部CT的人工智能辅助检测(computed tomography computer-aided detection,CT-CAD)相关研究则在PTB鉴别诊断、疗效及耐药性预测、科研辅助等领域,建立了多种AI模型并开展临床测试,且在PTB诊疗实践中展现出重要应用价值。作者综述CT-CAD在上述领域的研究进展,剖析其临床转化过程中存在的瓶颈问题,旨在为CT-CAD在PTB诊疗中的高效应用提供科学依据。

关键词: 结核,肺, 体层摄像术, 人工智能, 诊断

Abstract:

Early accurate diagnosis and drug resistance identification play a crucial role in the prevention and control of pulmonary tuberculosis (PTB). Conventional laboratory tests have significant lag, while manual interpretation of chest imaging is prone to interference from subjective factors. However, artificial intelligence (AI) technology provides a new direction for optimizing PTB diagnosis and treatment process. At present, chest X-ray computer-aided detection (CXR-CAD) software has been applied in daily clinical work. For computed tomography computer-aided detection (CT-CAD), relevant studies have established a variety of AI models and conducted clinical tests in the fields of PTB differential diagnosis, effectiveness, drug resistance prediction, and scientific research assistance. These models have demonstrated important application value in PTB diagnosis and treatment practice. This study reviews the research progress of CT-CAD in the above-mentioned fields, analyzes bottleneck problems in its clinical transformation process, to provide a scientific basis for the efficient application of CT-CAD in PTB diagnosis and treatment.

Key words: Tuberculosis, pulmonary, Tomography, Artificial intelligence, Diagnosis

中图分类号:

张嘉诚, 唐神结, 侯代伦, 李亮. 胸部CT联合人工智能辅助检测在肺结核诊疗中的研究进展[J]. 结核与肺部疾病杂志, 2026, 7(1): 112-119. doi: 10.19983/j.issn.2096-8493.20250134

Zhang Jiacheng, Tang Shenjie, Hou Dailun, Li Liang. Advances in research on chest CT combined with artificial intelligence-assisted detection in the diagnosis and treatment of pulmonary tuberculosis[J]. Journal of Tuberculosis and Lung Disease, 2026, 7(1): 112-119. doi: 10.19983/j.issn.2096-8493.20250134

参考文献 52

[1]	Trajman A, Campbell JR, Kunor T, et al. Tuberculosis. The Lancet, 2025, 405(10481): 850-866. doi:10.1016/S0140-6736(24)02479-6.
[2]	胡鑫洋, 高静韬. 世界卫生组织《2024年全球结核病报告》解读. 结核与肺部疾病杂志, 2024, 5(6): 500-504. doi:10.19983/j.issn.2096-8493.2024164.
[3]	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. 中国防痨杂志, 2024, 46(9): 1098-1103. doi:10.19982/j.issn.1000-6621.20240123.
[4]	Zhang J, Huang T, He X, et al. Machine learning-based model assists in differentiating Mycobacterium avium Complex Pulmonary Disease from Pulmonary Tuberculosis: A Multicenter Study. J Imaging Inform Med, 2025. doi:10.1007/s10278-025-01486-7.
[5]	Liang S, Ma J, Wang G, et al. The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Frontiers in Medicine, 2022, 9: 935080. doi:10.3389/fmed.2022.935080.
[6]	高珊, 聂文娟, 侯代伦, 等. 非结核分枝杆菌肺病影像学表现及应用人工智能新技术的研究进展. 中国防痨杂志, 2024, 46(3): 362-366. doi:10.19982/j.issn.1000-6621.20230404.
[7]	Innes AL, Martinez A, Gao X, et al. Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam’s District Health Facilities: An Implementation Study. Trop Med Infect Dis, 2023, 8(11):488. doi:10.3390/tropicalmed8110488.
[8]	Yang Y, Zhang H, Gichoya JW, et al. The Limits of Fair Medical Imaging AI In Real-World Generalization. Nature Medicine, 2024, 30(10): 2838-2848. doi:10.1038/s41591-024-03113-4.
[9]	Gao S, Xu Z, Kang W, et al. Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening. BMC Medical Imaging, 2024, 24(1): 141. doi:10.1186/s12880-024-01288-3.
[10]	张嘉诚, 黄婷婷, 何旭, 等. 基于CT特征和机器学习的鸟-胞内分枝杆菌复合群肺病和初治肺结核鉴别诊断模型的构建与验证. 中国医学影像学杂志, 2024, 32(10): 1007-1013, 1039. doi:10.3969/j.issn.1005-5185.2024.10.007.
[11]	Li Y, Lv X, Wang B, et al. Predicting Egfr T790M Mutation In Brain Metastases Using Multisequence Mri-Based Radiomics Signature. Academic Radiology, 2023, 30(9): 1887-1895. doi:10.1016/j.acra.2022.12.030.
[12]	中国防痨协会影像专业分会, 中华医学会结核病学分会, 中国防痨协会标准化专业分会, 等. 胸部影像学检查人工智能辅助读片技术在肺结核患者发现中的应用专家共识. 中国防痨杂志, 2025, 47(9): 1093-1104. doi:10.19982/j.issn.1000-6621.20250234.
[13]	梁逸宁, 侯代伦. 结核病影像学诊断年度进展2024. 中华结核和呼吸杂志, 2025, 48(2): 175-180. doi:10.3760/cma.j.cn112147-20241031-00650.
[14]	Nyasulu PS, Doumbia CO, Ngah V, et al. Multidrug-resistant tuberculosis: latest opinions on epidemiology, rapid diagnosis and management. Curr Opin Pulm Med, 2024, 30(3):217-228. doi:10.1097/MCP.0000000000001070. pmid: 38488133
[15]	Xu CJ, Lu PX, Li CH, et al. Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis. Quant Imaging Med Surg, 2024, 14(1):1039-1060. doi:10.21037/qims-23-1223.
[16]	Lin M, Li T, Yang Y, et al. Improving Model Fairness In Image-Based Computer-Aided Diagnosis. Nat Commun, 2023, 14(1): 6261. doi:10.1038/s41467-023-41974-4. pmid: 37803009
[17]	Deshpande D, Pasipanodya JG, Mpagama SG, et al. Levofloxacin Pharmacokinetics/Pharmacodynamics, Dosing, Susceptibility Breakpoints, And Artificial Intelligence In The Treatment Of Multidrug-Resistant Tuberculosis. Clin Infect Dis, 2018, 67(Suppl_3): S293-S302. doi:10.1093/cid/ciy611.
[18]	Preda M, Tănase BC, Zob DL, et al. The Bidirectional Relationship between Pulmonary Tuberculosis and Lung Cancer. Int J Environ Res Public Health, 2023, 20(2):1282. doi:10.3390/ijerph20021282.
[19]	Lv X, Li Y, Ding C, et al. Early Treatment Monitoring of Multidrug-Resistant Tuberculosis Based on CT Radiomics of Cavity and Cavity Periphery. Eur Radiol Exp, 2025, 9(1): 43. doi:10.1186/s41747-025-00581-2. pmid: 40285894
[20]	Reid M, Agbassi YJP, Arinaminpathy N, et al. Scientific Advances and The End of Tuberculosis: a Report From The Lancet Commission On Tuberculosis. Lancet, 2023, 402(10411): 1473-1498. doi:10.1016/S0140-6736(23)01379-X. pmid: 37716363
[21]	World Health Organization. Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches. Geneva: World Health Organization, 2016.
[22]	World Health Organization. WHO Consolidated Guidelines on Tuberculosis. Module 2: Screening-Systematic Screening for Tuberculosis Disease. Geneva: World Health Organization, 2021.
[23]	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.
[24]	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.
[25]	Xu Z, Bagci U, Kubler A, et al. Computer-aided detection and quantification of cavitary tuberculosis from CT scans. Med Phys, 2013, 40(11): 113701. doi:10.1118/1.4824979.
[26]	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.
[27]	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.
[28]	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.
[29]	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.
[30]	Gopalaswamy R, Shanmugam S, Mondal R, et al. Of tuberculosis and non-tuberculous mycobacterial infections-a comparative analysis of epidemiology, diagnosis and treatment. J Biomed Sci, 2020, 27(1): 74. doi:10.1186/s12929-020-00667-6. pmid: 32552732
[31]	Hu Y, Zhong L, Liu H, et al. Lung CT-based multi-lesion radiomic model to differentiate between nontuberculous mycobacteria and mycobacterium tuberculosis. Med Phys, 2025, 52(2): 1086-1095. doi:10.1002/mp.17537.
[32]	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
[33]	李文婷, 王丽, 方勇, 等. 基于CT影像的深度学习模型在肺结核与非结核分枝杆菌肺病中的鉴别诊断价值. 中国防痨杂志, 2024, 46(10): 1236-1242. doi:10.19982/j.issn.1000-6621.20240263.
[34]	Li HL, Zhi RZ, Liu HS, et al. Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis. Front Public Health, 2025, 13: 1470072. doi:10.3389/fpubh.2025.1470072.
[35]	蔡久媺. 基于CT影像组学的计算机辅助鉴别周围型肺癌与肿块/结节型肺结核的研究. 大连:大连医科大学, 2020. doi:10.26994/d.cnki.gdlyu.2020.000756.
[36]	Gao X, Tan H, Zhu M, et al. Construction and validation of a clinical differentiation model between peripheral lung cancer and solitary pulmonary tuberculosis. Lung Cancer, 2024, 193: 107851. doi:10.1016/j.lungcan.2024.107851.
[37]	段慧, 韩丹, 康绍磊, 等. 基于AI数据分析对不典型肺结核误诊为周围型肺癌的临床研究. 临床放射学杂志, 2021, 40(7): 1312-1315. doi: 10.13437/j.cnki.jcr.2021.07.015.
[38]	Zhao W, Xiong Z, Jiang Y, et al. Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses. J Cancer Res Clin Oncol, 2023, 149(7): 3395-3408. doi:10.1007/s00432-022-04256-y.
[39]	李多, 吕平欣. 推动人工智能技术在肺结核影像领域的发展和运用. 中国防痨杂志, 2024, 46(3): 253-259. doi:10.19982/j.issn.1000-6621.20240013.
[40]	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
[41]	Li Y, Xu Z, Lv X, et al. Radiomics Analysis of Lung CT For Multidrug Resistance Prediction In Active Tuberculosis: a Multicentre Study. Eur Radiol, 2023, 33(9):6308-6317. doi:10.1007/s00330-023-09589-x. pmid: 37004571
[42]	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, 2022, 33(1): 391-400. doi:10.1007/s00330-022-08997-9. pmid: 35852573
[43]	Jiang F, Xu C, Wang Y, et al. A CT-Based Radiomics Analyses For Differentiating Drug Resistant And Drug-Sensitive Pulmonary Tuberculosis. BMC Med Imaging, 2024, 24(1): 307. doi:10.1186/s12880-024-01481-4.
[44]	Tendolkar MS, Tyagi R, Handa A. Review Of Advances In Diagnosis And Treatment Of Pulmonary Tuberculosis. Indian J Tuberc, 2021, 68(4): 510-515. doi:10.1016/j.ijtb.2021.07.002. pmid: 34752322
[45]	Lv X, Li Y, Cai B, et al. Utility Of Machine Learning And Radiomics Based On Cavity For Predicting The Therapeutic Response Of Mdr-Tb. Infect Drug Resist, 2023, 16: 6893-6904. doi:10.2147/IDR.S435984. pmid: 37920476
[46]	樊雅欣, 张惠勇, 苏奔, 等. CT影像组学诊断与治疗肺结核:临床应用现状及前景. 中国医学影像技术, 2024, 40(12): 1957-1960. doi:10.13929/j.issn.1003-3289.2024.12.032.
[47]	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.
[48]	Bougourzi F, Hadid A. Recent Advances In Medical Imaging Segmentation: A Survey. Arxiv, 2025. doi: 10.48550/arXiv.2505.09274.
[49]	Lumamba KD, Wells G, Naicker D, et al. Computer Vision Applications For The Detection Or Analysis Of Tuberculosis Using Digitised Human Lung Tissue Images-a Systematic Review. BMC Med Imaging, 2024, 24(1): 298. doi:10.1186/s12880-024-01443-w.
[50]	Xu Z, Bagci U, Kubler A, et al. Computer-Aided Detection And Quantification Of Cavitary Tuberculosis From CT Scans. Med Phys, 2013, 40(11): 113701. doi:10.1118/1.4824979.
[51]	Yoon I, Hong JH, Witanto JN, et al. Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification. Quant Imaging Med Surg, 2023, 13(2): 747-762. doi:10.21037/qims-22-620.
[52]	巩盼, 李星宇, 来坤, 等. 基于定量CT检测浸润性肺结核病变的可行性研究. 临床放射学杂志, 2024, 43(4): 567-573. doi:10.13437/j.cnki.jcr.2024.04.021.

胸部CT联合人工智能辅助检测在肺结核诊疗中的研究进展

Advances in research on chest CT combined with artificial intelligence-assisted detection in the diagnosis and treatment of pulmonary tuberculosis

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[5]	中国中西医结合学会中华中医药学会中华医学会. 耐药肺结核中西医结合诊疗指南[J]. 结核与肺部疾病杂志, 2025, 6(5): 477-494.
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[9]	中国防痨协会结核病控制专业分会, 中国防痨协会标准化专业分会, 中国防痨协会老年结核病防治专业分会. 结核分枝杆菌感染检测技术应用专家共识[J]. 结核与肺部疾病杂志, 2025, 6(4): 365-381.
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