| 1 |
Exarchos K, Aggelopoulou A, Oikonomou A, et al. Review of artificial intelligence techniques in chronic obstructive lung disease[J]. IEEE J Biomed Health Inform, 2022, 26(5): 2331-2338.
|
| 2 |
Howard J. Artificial intelligence: Implications for the future of work[J]. Am J Ind Med, 2019, 62(11): 917-926.
|
| 3 |
Waqas A, Bui MM, Glassy EF, et al. Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models[J]. Lab Invest, 2023, 103(11): 100255.
|
| 4 |
Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making[J]. JAMA Surg, 2020, 155(2): 148-158.
|
| 5 |
Bhinder B, Gilvary C, Madhukar NS, et al. Artificial intelligence in cancer research and precision medicine[J]. Cancer Discov, 2021, 11(4): 900-915.
|
| 6 |
Potnis KC, Ross JS, Aneja S, et al. Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations[J]. JAMA Intern Med, 2022, 182(12): 1306-1312.
|
| 7 |
Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: potential resources for the infection clinician[J]. J Infect, 2023, 87(4): 287-294.
|
| 8 |
Van Breugel M, Fehrmann RSN, Bügel M, et al. Current state and prospects of artificial intelligence in allergy[J]. Allergy, 2023, 78(10): 2623-2643.
|
| 9 |
Mullowney MW, Duncan KR, Elsayed SS, et al. Artificial intelligence for natural product drug discovery[J]. Nat Rev Drug Discov, 2023, 22(11): 895-916.
|
| 10 |
Zhang C, Xu J, Tang R, et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment[J]. J Hematol Oncol, 2023, 16(1): 114.
|
| 11 |
吴煜芳，林凯洵，熊珈艺，等．《2026年GOLD慢性阻塞性肺疾病诊断、管理及预防全球策略》更新要点解读[J]. 实用心脑肺血管病杂志，2026, 34(2): 1-11.
|
| 12 |
迟春花，荆冠宁. 数智赋能慢性阻塞性肺疾病基层管理：从理念到实践的新跨越[J]. 中华全科医师杂志，2026, 25(7): 771-775.
|
| 13 |
Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for the Prevention, Diagnosis and Management of COPD: 2026 Report[R/OL]. (2025-11-17) [2025-12-08].
URL
|
| 14 |
Liu Z, Li J, Li B, et al. Utility of an artificial intelligence-based lung CT airway model in the quantitative evaluation of large and small airway lesions in patients with chronic obstructive pulmonary disease[J]. BMC Pulmonary Medicine, 2025, 25: 371.
|
| 15 |
Wu CT, Li GH, Huang CT, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study[J]. JMIR mHealth and uHealth, 2021, 9(5): e22591.
|
| 16 |
Otaru Abdulrazak Jinadu, Abubakar Fatima Zahra, Ibitoye Morufu Olusola. Breathing life into data: A critical review of artificial intelligence and computational modelling in COPD precision management[J]. Respir Med, 2026, 262: 109121.
|
| 17 |
Lamprecht B, Soriano JB, Studnicka M, et al. Determinants of underdiagnosis of COPD in national and international surveys[J]. Chest, 2015, 148(4): 971-985.
|
| 18 |
中华医学会呼吸病学分会肺功能学组. 成人肺功能检查技术进展及临床应用推荐指南(2025版) [J/OL]. 中华肺部疾病杂志(电子版), 2025, 18(2): 197-212.
|
| 19 |
佟赫，丛舒，方利文，等. 2019-2020年中国40岁及以上人群肺功能检查状况分析[J]. 中华流行病学杂志，2023, 44: 727-734.
|
| 20 |
中华医学会，中华医学会杂志社，中华医学会全科医学分会，等. 中国常规肺功能检查基层指南(2024年)[J]. 中华全科医师杂志，2025, 24(2): 121-137.
|
| 21 |
中国医学装备协会呼吸病学装备技术专业委员会，中华医学会呼吸病学分会慢性阻塞性肺疾病学组，广东省药学会. 稳定期慢性阻塞性肺疾病医药共管专家共识[J]. 中华结核和呼吸杂志，2022, 45(10): 980-987.
|
| 22 |
Singh D, Agusti A, Anzueto A, et al. Global strategy for the diagnosis，management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019[J]. Eur Respir J, 2019, 53(5): 1900164.
|
| 23 |
Kim LHY, Saleh C, Whalen-Browne A, et al. Triple vs dual inhaler therapy and asthma outcomes in moderate to severe asthma: a systematic review and Meta-analysis[J]. Jama, 2021, 325(24): 2466-2479.
|
| 24 |
Bhatt SP, Rabe KF, Hanania N A, et al. Dupilumab for COPD with blood eosinophil evidence of type 2 inflammation[J]. N Engl J Med, 2024, 390(24): 2274-2283.
|
| 25 |
Hong L, Cheng X, Zheng D. Application of artificial intelligence in emergency nursing of patients with chronic obstructive pulmonary disease[J]. Contrast Media Mol Imag, 2021, 2021: 6423398.
|
| 26 |
韩姚嫔，贺斌峰，张静. 早发型慢性阻塞性肺疾病和慢性阻塞性肺疾病前期的研究进展[J]. 中华预防医学杂志，2023, 57(8): 1164-1170.
|
| 27 |
中华医学会，中华医学会杂志社，中华医学会全科医学分会，等. 中国慢性阻塞性肺疾病基层诊疗与管理指南(2024年)[J]. 中华全科医师杂志，2024, 23(6): 578-602.
|
| 28 |
Tang LYW, Coxson HO, Lam S, et al. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT[J]. Lancet Digit Health, 2020, 2(5): e259-e267.
|
| 29 |
Angelini ED, Yang J, Balte PP, et al. Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans[J]. Thorax, 2023, 78(11): 1067-1079.
|
| 30 |
Li Z, Liu L, Zhang Z, et al. A novel CT-based radiomics features analysis for identification and severity staging of COPD[J]. Acad Radiol, 2022, 29(5): 663-673.
|
| 31 |
Makimoto K, Au R, Moslemi A, et al. Comparison of feature selection methods and machine learning classifiers for predicting chronic obstructive pulmonary disease using texture-based CT lung radiomic features[J]. Acad Radiol, 2023, 30(5): 900-910.
|
| 32 |
Makimoto K, Hogg JC, Bourbeau J, et al. CT imaging with machine learning for predicting progression to COPD in individuals at risk[J]. Chest, 2023, 164(5): 1139-1149.
|
| 33 |
Zhu Z, Zhao S, Li J, et al. Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease[J]. Respir Res, 2024, 25(1): 167.
|
| 34 |
Sun J, Liao X, Yan Y, et al. Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach[J]. Eur Radiol, 2022, 32(8): 5319-5329.
|
| 35 |
Wang X, Qiao Y, Cui Y, et al. An explainable artificial intelligence framework for risk prediction of COPD in smokers[J]. BMC Public Health, 2023, 23(1): 2164.
|
| 36 |
Lin F, Zhang Z, Wang J, et al. AutoCOPD-A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study[J]. E Clin Med, 2025, 82: 103166.
|
| 37 |
Melekoglu E, Kocabicak U, Uçar MK, et al. A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence[J]. PeerJ Comput Sci, 2022, 8: e1188.
|
| 38 |
Haider NS, Singh BK, Periyasamy R, et al. Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm[J]. J Med Syst, 2019, 43(8): 255.
|
| 39 |
Yang Z, Zheng Y, Zhang L, et al. Screening the best risk model and susceptibility SNPs for chronic obstructive pulmonary disease (COPD) based on machine learning algorithms[J]. Int J Chron Obstruct Pulmon Dis, 2024, 19: 2397-2414.
|
| 40 |
Zhu Z, Zeng Z, Song B, et al. Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning[J]. J Cell Mol Med, 2024, 28(18): e70107.
|
| 41 |
林凯洵，张昊天，王妍，等．《2025年GOLD慢性阻塞性肺疾病诊断、管理及预防全球策略》更新要点解读[J]. 实用心脑肺血管病杂志，2025, 33(2): 1-10.
|
| 42 |
Çalli E, Murphy K, Scholten ET, et al. Explainable emphysema detection on chest radiographs with deep learning[J]. PLoS One, 2022, 17(7): e0267539.
|
| 43 |
Xie W, Jacobs C, Charbonnier JP, et al. Emphysema subtyping on thoracic computed tomography scans using deep neural networks[J]. Sci Rep, 2023, 13(1): 14147.
|
| 44 |
Ebrahimian S, Digumarthy SR, Bizzo B, et al. Artificial intelligence has similar performance to subjective assessment of emphysema severity on chest CT[J]. Acad Radiol, 2022, 29(8): 1189-1195.
|
| 45 |
Fischer AM, Varga-Szemes A, Martin SS, et al. Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers[J]. J Thorac Imag, 2020, 35 (Suppl 1): S28-S34.
|
| 46 |
Zhao M, Wu Y, Li Y, et al. Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images[J]. BMC Pulm Med, 2024, 24(1): 294.
|
| 47 |
Almeida SD, Norajitra T, Lüth CT, et al. Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT[J]. Eur Radiol, 2024, 34(7): 4379-4392.
|
| 48 |
Chaudhary MFA, Awan HA, Gerard SE, et al. Deep learning estimation of small airway disease from inspiratory chest computed tomography: clinical validation, repeatability, and associations with adverse clinical outcomes in chronic obstructive pulmonary disease[J]. Am J Respir Crit Care Med, 2025, 211(7): 1185-1195.
|
| 49 |
Yin C, Udrescu M, Gupta G, et al. Fractional dynamics foster deep learning of COPD stage prediction[J]. Adv Sci (Weinh), 2023, 10(12): e2203485.
|
| 50 |
Wedzicha JA, Seemungal TA. COPD exacerbations: defining their cause and prevention[J]. Lancet, 2007, 370(9589): 786-796.
|
| 51 |
杨华静，王凤燕，梁振宇，等. 慢性阻塞性肺疾病年度进展2025[J]. 中华结核和呼吸杂志，2026, 49(1): 65-70.
|
| 52 |
Li R, Song M, Wang R, et al. Can CT-based arterial and venous morphological markers of chronic obstructive pulmonary disease explain pulmonary vascular remodeling？[J]. Acad Radiol, 2024, 31(1): 22-34.
|
| 53 |
Lopez K, Li H, Lipkin-Moore Z, et al. Deep learning prediction of hospital readmissions for asthma and COPD[J]. Respir Res, 2023, 24(1): 311.
|
| 54 |
Panettieri RA, JR., Roy J, Gontarczyk Uczkowski N, et al. Leveraging machine learning and real-world data to predict chronic obstructive pulmonary disease exacerbations[J]. Int J Chron Obstruct Pulmon Dis, 2025, 20: 3451-3459.
|
| 55 |
Peng J, Zhou M, Zou K, et al. Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning[J]. BMC Med Inform Decis Mak, 2021, 21(1): 348.
|
| 56 |
何冬雪，吴红梅，李建洁，等. 可穿戴设备在慢性阻塞性肺疾病患者全程规范化管理中的应用现状与研究进展[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(4): 691-695.
|