人工智能在结缔组织病相关间质性肺病进展型肺纤维化中的应用及研究进展
Advances and Research Progress in the Application of Artificial Intelligence for Progressive Pulmonary Fibrosis in Connective Tissue Disease-Associated Interstitial Lung Disease
DOI: 10.12677/acm.2026.1652018, PDF,   
作者: 王佳琳:江汉大学医学部,湖北 武汉;孙 珊:华中科技大学同济医学院附属武汉中心医院全科医学科,湖北 武汉;林桂丽:汕头大学医学院第一附属医院全科医学分院,广东 汕头;刘晓帆*:华中科技大学同济医学院附属武汉中心医院呼吸与危重症医学科,湖北 武汉
关键词: 结缔组织病相关间质性肺病进展型肺纤维化人工智能Connective Tissue Disease-Associated Interstitial Lung Disease Progressive Pulmonary Fibrosis Artificial Intelligence
摘要: 结缔组织病相关间质性肺病(CTD-ILD)是结缔组织病(CTD)引发的肺部并发症之一。其中一部分患者会发展为进展型肺纤维化(PPF)表型,在确诊后患者肺功能不可逆的肺损伤、死亡率增加。近年来,基于人工智能在影像学上取得显著的进展,本文系统地阐述了AI在CTD-ILD进展型肺纤维化中的应用现状、关键技术、临床转化挑战及未来发展方向。
Abstract: Connective Tissue Disease-Associated Interstitial Lung Disease (CTD-ILD) is one of the pulmonary complications caused by connective tissue diseases (CTD). A portion of these patients will develop the progressive pulmonary fibrosis (PPF) phenotype. After diagnosis, patients suffer irreversible lung damage and an increased mortality rate. In recent years, with significant progress in artificial intelligence in imaging, this article systematically elaborates on the current application status, key technologies, clinical transformation challenges, and future development directions of AI in CTD-ILD progressive pulmonary fibrosis.
文章引用:王佳琳, 孙珊, 林桂丽, 刘晓帆. 人工智能在结缔组织病相关间质性肺病进展型肺纤维化中的应用及研究进展[J]. 临床医学进展, 2026, 16(5): 2107-2115. https://doi.org/10.12677/acm.2026.1652018

参考文献

[1] 邹庆华, 路跃武, 周京国, 等. 结缔组织病相关间质性肺疾病诊疗规范[J]. 中华内科杂志, 2022, 61(11): 1217-1223.
[2] Guiot, J., Miedema, J., Cordeiro, A., De Vries-Bouwstra, J.K., Dimitroulas, T., Søndergaard, K., et al. (2024) Practical Guidance for the Early Recognition and Follow-Up of Patients with Connective Tissue Disease-Related Interstitial Lung Disease. Autoimmunity Reviews, 23, Article ID: 103582. [Google Scholar] [CrossRef] [PubMed]
[3] 阿依尼尕尔·阿布力孜, 刘晖. 不同结缔组织病相关间质性肺病临床特点研究进展[J]. 临床医学进展, 2023, 13(2): 2006-2011.
[4] Wells, A.U., Brown, K.K., Flaherty, K.R., Kolb, M. and Thannickal, V.J. (2018) What’s in a Name? That Which We Call IPF, by Any Other Name Would Act the Same. European Respiratory Journal, 51, Article ID: 1800692. [Google Scholar] [CrossRef] [PubMed]
[5] Raghu, G., Remy-Jardin, M., Richeldi, L., et al. (2022) Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. American Journal of Respiratory and Critical Care Medicine, 205, e18-e47.
[6] Chiu, Y., Koops, M.F.M., Voortman, M., van Es, H.W., Langezaal, L.C.M., Welsing, P.M.J., et al. (2023) Prognostication of Progressive Pulmonary Fibrosis in Connective Tissue Disease-Associated Interstitial Lung Diseases: A Cohort Study. Frontiers in Medicine, 10, Article ID: 1106560. [Google Scholar] [CrossRef] [PubMed]
[7] Mei, X., Liu, Z., Singh, A., Lange, M., Boddu, P., Gong, J.Q.X., et al. (2023) Interstitial Lung Disease Diagnosis and Prognosis Using an AI System Integrating Longitudinal Data. Nature Communications, 14, Article No. 2272. [Google Scholar] [CrossRef] [PubMed]
[8] Spagnolo, P., Distler, O., Ryerson, C.J., Tzouvelekis, A., Lee, J.S., Bonella, F., et al. (2021) Mechanisms of Progressive Fibrosis in Connective Tissue Disease (CTD)-Associated Interstitial Lung Diseases (ILDs). Annals of the Rheumatic Diseases, 80, 143-150. [Google Scholar] [CrossRef] [PubMed]
[9] 崔天晓, 崔挺, 叶·叶尔丁其木克, 等. 结缔组织病并发间质性肺疾病风险的列线图预测模型构建[J]. 检验医学与临床, 2024, 21(15): 2145-2149+2154.
[10] 吕倩, 张可, 申玉霞, 等. 结缔组织病相关间质性肺疾病呈进展性纤维化表型的临床特征及危险因素分析[J]. 中国现代医学杂志, 2023, 33(21): 85-93.
[11] 刘欣欣, 周晓蕾, 贾要丽, 等. 构建CT结合血清学指标模型预测结缔组织病相关肺纤维化的进展[J]. 中国呼吸与危重监护杂志, 2024, 23(6): 406-413.
[12] Khalid, A., Mushtaq, M.M., Sattar, S., Soe, Y.N., Ismail, S., Haris, M., et al. (2025) Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review. Cureus, 17, e87461. [Google Scholar] [CrossRef] [PubMed]
[13] 孙海双, 杨晓燕, 刘敏, 等. 人工智能在间质性肺疾病评价中的应用进展[J]. 中国医学影像学杂志, 2022, 30(5): 509-513.
[14] 刘书燕, 鄂林宁. 人工智能在间质性肺病中的应用进展[J]. 中华放射学杂志, 2024, 58(8): 873-876.
[15] Yi, E.S., Wawryko, P. and Ryu, J.H. (2024) Diagnosis of Interstitial Lung Diseases: From Averill A. Liebow to Artificial Intelligence. Journal of Pathology and Translational Medicine, 58, 1-11. [Google Scholar] [CrossRef] [PubMed]
[16] Hoffmann, T., Teichgräber, U., Brüheim, L.B., Lassen-Schmidt, B., Renz, D., Weise, T., et al. (2025) The Association of Symptoms, Pulmonary Function Test and Computed Tomography in Interstitial Lung Disease at the Onset of Connective Tissue Disease: An Observational Study with Artificial Intelligence Analysis of High-Resolution Computed Tomography. Rheumatology International, 45, Article No. 194. [Google Scholar] [CrossRef] [PubMed]
[17] 何炎芮, 王平, 尹成胜, 等. 系统性自身免疫性风湿病患者间质性肺疾病的筛查与监测和治疗: 2023年美国风湿学会/美国胸科医师学会指南摘译[J]. 中华结核和呼吸杂志, 2025, 48(4): 396-400.
[18] 刘燕, 施春花. 结缔组织病相关性间质性肺疾病诊断的研究进展[J]. 实用临床医学, 2023, 24(4): 118-124.
[19] Antoniou, K.M., Distler, O., Gheorghiu, A.M., et al. (2025) ERS/EULAR Clinical Practice Guidelines for Connective Tissue Disease-Associated Interstitial Lung Disease Developed by the Task Force for Connective Tissue Disease-Associated Interstitial Lung Disease of the European Respiratory Society (ERS) and the European Alliance of Associations for Rheumatology (EULAR): Endorsed by the European Reference Network on Rare Respiratory Diseases (ERN-LUNG). Annals of the Rheumatic Diseases, 85, 22-60.
[20] Sica, G., D’Agnano, V., Bate, S.T., Romano, F., Viglione, V., Franzese, L., et al. (2025) Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis. Diagnostics (Basel), 15, Article No. 278. [Google Scholar] [CrossRef] [PubMed]
[21] 王珂, 冀韩英, 黄晓旗. 基于定量CT评估CTD-ILD的研究进展[J]. 临床医学进展, 2025, 15(6): 1480-1487.
[22] Ufuk, F., Demirci, M. and Altinisik, G. (2020) Quantitative Computed Tomography Assessment for Systemic Sclerosis-Related Interstitial Lung Disease: Comparison of Different Methods. European Radiology, 30, 4369-4380. [Google Scholar] [CrossRef] [PubMed]
[23] Guisado-Vasco, P., Silva, M., Duarte-Millán, M.A., Sambataro, G., Bertolazzi, C., Pavone, M., et al. (2019) Quantitative Assessment of Interstitial Lung Disease in Sjögren’s Syndrome. PLOS ONE, 14, e0224772. [Google Scholar] [CrossRef] [PubMed]
[24] Roncella, C., Barsotti, S., Valentini, A., Cavagna, L., Castellana, R., Cioffi, E., et al. (2022) Evaluation of Interstitial Lung Disease in Idiopathic Inflammatory Myopathies through Semiquantitative and Quantitative Analysis of Lung Computed Tomography. Journal of Thoracic Imaging, 37, 344-351. [Google Scholar] [CrossRef] [PubMed]
[25] Rea, G., De Martino, M., Capaccio, A., Dolce, P., Valente, T., Castaldo, S., et al. (2020) Comparative Analysis of Density Histograms and Visual Scores in Incremental and Volumetric High-Resolution Computed Tomography of the Chest in Idiopathic Pulmonary Fibrosis Patients. La Radiologia Medica, 126, 599-607. [Google Scholar] [CrossRef] [PubMed]
[26] Widell, J. and Lidén, M. (2020) Interobserver Variability in High-Resolution CT of the Lungs. European Journal of Radiology Open, 7, Article ID: 100228. [Google Scholar] [CrossRef] [PubMed]
[27] Koh, S.Y., Lee, J.H., Park, H. and Goo, J.M. (2024) Value of CT Quantification in Progressive Fibrosing Interstitial Lung Disease: A Deep Learning Approach. European Radiology, 34, 4195-4205. [Google Scholar] [CrossRef] [PubMed]
[28] Chen, L., Zhu, M., Lu, H., Yang, T., Li, W., Zhang, Y., et al. (2022) Quantitative Evaluation of Disease Severity in Connective Tissue Disease-Associated Interstitial Lung Disease by Dual-Energy Computed Tomography. Respiratory Research, 23, Article No. 47. [Google Scholar] [CrossRef] [PubMed]
[29] 郭红红, 曹珊, 杨晨, 等. 结缔组织病相关间质性肺疾病的CT定量分析研究进展[J]. 放射学实践, 2023, 38(11): 1467-1471.
[30] Hamada, A., Yasaka, K., Hatano, S., Kurokawa, M., Inui, S., Kubo, T., et al. (2024) Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis. Canadian Association of Radiologists Journal, 75, 542-548. [Google Scholar] [CrossRef] [PubMed]
[31] Zheng, B., Marinescu, D., Hague, C.J., Muller, N.L., Murphy, D., Churg, A., et al. (2024) Lung Imaging Patterns in Connective Tissue Disease-Associated Interstitial Lung Disease Impact Prognosis and Immunosuppression Response. Rheumatology, 63, 2734-2740. [Google Scholar] [CrossRef] [PubMed]
[32] Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. and Mougiakakou, S. (2016) Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging, 35, 1207-1216. [Google Scholar] [CrossRef] [PubMed]
[33] Walsh, S.L.F., Calandriello, L., Silva, M. and Sverzellati, N. (2018) Deep Learning for Classifying Fibrotic Lung Disease on High-Resolution Computed Tomography: A Case-Cohort Study. The Lancet Respiratory Medicine, 6, 837-845. [Google Scholar] [CrossRef] [PubMed]
[34] Christe, A., Peters, A.A., Drakopoulos, D., Heverhagen, J.T., Geiser, T., Stathopoulou, T., et al. (2019) Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Investigative Radiology, 54, 627-632. [Google Scholar] [CrossRef] [PubMed]
[35] Choe, J., Hwang, H.J., Seo, J.B., Lee, S.M., Yun, J., Kim, M., et al. (2022) Content-Based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology, 302, 187-197. [Google Scholar] [CrossRef] [PubMed]
[36] Su, N., Hou, F., Zheng, W., Wu, Z. and E, L. (2023) Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients with Connective Tissue Disease-Associated Interstitial Lung Disease. Journal of Computer Assisted Tomography, 47, 738-745. [Google Scholar] [CrossRef] [PubMed]
[37] Russo, A., Patanè, V., Oliva, A., Viglione, V., Franzese, L., Forte, G., et al. (2025) AI-Based HRCT Quantification in Connective Tissue Disease-Associated Interstitial Lung Disease. Diagnostics, 15, Article No. 2179. [Google Scholar] [CrossRef
[38] Long, B., Li, R., Wang, R., Yin, A., Zhuang, Z., Jing, Y., et al. (2025) A Computed Tomography-Based Deep Learning Radiomics Model for Predicting the Gender-Age-Physiology Stage of Patients with Connective Tissue Disease-Associated Interstitial Lung Disease. Computers in Biology and Medicine, 191, Article ID: 110128. [Google Scholar] [CrossRef] [PubMed]
[39] Barnes, H., Humphries, S.M., George, P.M., Assayag, D., Glaspole, I., Mackintosh, J.A., et al. (2023) Machine Learning in Radiology: The New Frontier in Interstitial Lung Diseases. The Lancet Digital Health, 5, e41-e50. [Google Scholar] [CrossRef] [PubMed]
[40] Martini, K., Baessler, B., Bogowicz, M., Blüthgen, C., Mannil, M., Tanadini-Lang, S., et al. (2021) Applicability of Radiomics in Interstitial Lung Disease Associated with Systemic Sclerosis: Proof of Concept. European Radiology, 31, 1987-1998. [Google Scholar] [CrossRef] [PubMed]
[41] Jiang, X., Su, N., Quan, S., E, L. and Li, R. (2023) Computed Tomography Radiomics-Based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-Associated Interstitial Lung Disease. Academic Radiology, 30, 2598-2605. [Google Scholar] [CrossRef] [PubMed]
[42] Long, B., Li, R., Wang, R., Yin, A., Zhuang, Z., Jing, Y., et al. (2025) A Computed Tomography-Based Deep Learning Radiomics Model for Predicting the Gender-Age-Physiology Stage of Patients with Connective Tissue Disease-Associated Interstitial Lung Disease. Computers in Biology and Medicine, 191, Article ID: 110128. [Google Scholar] [CrossRef] [PubMed]
[43] Molina-Molina, M., Castellví, I., Valenzuela, C., Ramirez, J., Rodríguez Portal, J.A., Franquet, T., et al. (2022) Management of Progressive Pulmonary Fibrosis Associated with Connective Tissue Disease. Expert Review of Respiratory Medicine, 16, 765-774. [Google Scholar] [CrossRef] [PubMed]
[44] Wijsenbeek, M., Kreuter, M., Olson, A., Fischer, A., Bendstrup, E., Wells, C.D., et al. (2019) Progressive Fibrosing Interstitial Lung Diseases: Current Practice in Diagnosis and Management. Current Medical Research and Opinion, 35, 2015-2024. [Google Scholar] [CrossRef] [PubMed]
[45] Humphries, S.M., Thieke, D., Baraghoshi, D., Strand, M.J., Swigris, J.J., Chae, K.J., et al. (2024) Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes. American Journal of Respiratory and Critical Care Medicine, 209, 1121-1131. [Google Scholar] [CrossRef] [PubMed]
[46] Ito, Y., Ichikawa, Y., Murashima, S., Sakuma, H., Iwasawa, T., Arinuma, Y., et al. (2024) Novel Deep-Learning Analysis for Connective Tissue Disease-Related Interstitial Lung Disease Extent Assessment on CT: A Preliminary Cross-Sectional Study. Rheumatology, 64, SI14-SI20. [Google Scholar] [CrossRef] [PubMed]
[47] Guiot, J., Henket, M., Gester, F., André, B., Ernst, B., Frix, A., et al. (2025) Automated AI-Based Image Analysis for Quantification and Prediction of Interstitial Lung Disease in Systemic Sclerosis Patients. Respiratory Research, 26, Article No. 39. [Google Scholar] [CrossRef] [PubMed]
[48] Zhao, J., Long, Y., Li, S., Li, X., Zhang, Y., Hu, J., et al. (2024) Use of Artificial Intelligence Algorithms to Analyse Systemic Sclerosis-Interstitial Lung Disease Imaging Features. Rheumatology International, 44, 2027-2041. [Google Scholar] [CrossRef] [PubMed]
[49] Montero, I.E., Hernandez-Gonzalez, F. and Sellares, J. (2025) Epidemiology and Prognosis of Progressive Pulmonary Fibrosis: A Literature Review. Pulmonary Therapy, 11, 347-363. [Google Scholar] [CrossRef] [PubMed]
[50] Johkoh, T., Müller, N.L., Cartier, Y., Kavanagh, P.V., Hartman, T.E., Akira, M., et al. (1999) Idiopathic Interstitial Pneumonias: Diagnostic Accuracy of Thin-Section CT in 129 Patients. Radiology, 211, 555-560. [Google Scholar] [CrossRef] [PubMed]
[51] Rea, G., Sverzellati, N., Bocchino, M., Lieto, R., Milanese, G., D’Alto, M., et al. (2023) Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology”. Diagnostics, 13, Article No. 2333. [Google Scholar] [CrossRef] [PubMed]
[52] Bocchino, M., Bruzzese, D., D’Alto, M., Argiento, P., Borgia, A., Capaccio, A., et al. (2019) Performance of a New Quantitative Computed Tomography Index for Interstitial Lung Disease Assessment in Systemic Sclerosis. Scientific Reports, 9, Article No. 9468. [Google Scholar] [CrossRef] [PubMed]
[53] Qiang, Y., Wang, H., Ni, Y., Wang, J., Liu, A., Yang, H., et al. (2024) Development of a Machine Learning Model in Prediction of the Rapid Progression of Interstitial Lung Disease in Patients with Idiopathic Inflammatory Myopathy. Quantitative Imaging in Medicine and Surgery, 14, 9258-9275. [Google Scholar] [CrossRef] [PubMed]
[54] Battaglia, C., Pelaia, C., Lupia, C., Mondelli, A., Turco, F., Zaffino, P., et al. (2025) Quantification and Analysis of Lung Involvement by Artificial Intelligence in Patients with Progressive Pulmonary Fibrosis Treated with Nintedanib. Medicina, 61, Article No. 1646. [Google Scholar] [CrossRef
[55] 王远旭, 刘梦伟. 人工智能医疗背景下医患关系面临的伦理挑战及对策建议[J]. 中国医学伦理学, 2022, 35(7): 764-768+789.
[56] Hwang, J., Lee, T., Lee, H. and Byun, S. (2022) A Clinical Decision Support System for Sleep Staging Tasks with Explanations from Artificial Intelligence: User-Centered Design and Evaluation Study. Journal of Medical Internet Research, 24, e28659. [Google Scholar] [CrossRef] [PubMed]