Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

ERS Congress 2024

Keep up to date on the latest news from the annual European Respiratory Society conference.
ADVANCED SEARCH
Log in
Top
Respiratory Research
Published in:

Open Access 22-08-2024 | Chronic Obstructive Lung Disease | Review

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Authors: Yanan Wu, Shuyue Xia, Zhenyu Liang, Rongchang Chen, Shouliang Qi

Published in: Respiratory Research | Issue 1/2024

Login to get access

Abstract

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn’t just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.
Literature
1.
Agustí A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, et al. Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary. Am J Respir Crit Care Med. 2023;207(7):819–37.PubMedPubMedCentralCrossRef
2.
Washko GR, Coxson HO, O’Donnell DE, Aaron SD. CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise. Lancet Respir Med. 2017;5(11):903–8.PubMedCrossRef
3.
Doke PP. Chronic respiratory diseases: a rapidly emerging public health menace. Indian J Public Health. 2023;67(2):192–6.PubMedCrossRef
4.
Momtazmanesh S, Moghaddam SS, Ghamari SH, Rad EM, Rezaei N, Shobeiri P, et al. Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019. EClinicalMedicine. 2023;59:101936.
5.
Washko GR, Coxson HO, O"Donnell DE, Aaron SD. CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise. Lancet Respir Med. 2017;5(11):903–8.
6.
Bhatt SP, Washko GR, Hoffman EA, Newell JD, Bodduluri S, Diaz AA, et al. Imaging Advances in Chronic Obstructive Pulmonary Disease: Insights from COPDGene. Am J Respir Crit Care Med. 2018;199(3):286–301.CrossRef
7.
8.
Bailey KL. The importance of the assessment of pulmonary function in COPD. Med Clin N Am. 2012;96(4):745–52.PubMedCrossRef
9.
Bodduluri S, Nakhmani A, Kandhare P, Patel T, Reinhardt J, Wilson C, et al. CT-Based Airway Surface Area to Volume Ratio Is Associated with Lung Function Decline in Chronic Obstructive Pulmonary Disease (COPD). In: A98. PHENOTYPING COPD AND PREDICTING THE DISEASE PROGRESSION. New York: American Thoracic Society; 2019. p. A2411.
10.
Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology. 2015;277(1):192–205.PubMedCrossRef
11.
12.
Gawlitza J, Sturm T, Spohrer K, Henzler T, Akin I, Schönberg S, et al. Predicting pulmonary function testing from quantified computed tomography using machine learning algorithms in patients with COPD. Diagnostics. 2019;9(1):33.PubMedPubMedCentralCrossRef
13.
Konietzke P, Wielpütz MO, Wagner WL, Wuennemann F, Kauczor HU, Heussel CP, et al. Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval. Eur Radiol. 2020;30:2502–12.PubMedCrossRef
14.
Bodduluri S, Reinhardt JM, Hoffman EA, Newell JD, Bhatt SP. Recent Advances in Computed Tomography Imaging in Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc. 2018;15(3):281–9.PubMedPubMedCentralCrossRef
15.
Kauczor HU, Wielpütz MO, Jobst BJ, Weinheimer O, Gompelmann D, Herth FJ, et al. Computed tomography imaging for novel therapies of chronic obstructive pulmonary disease. J Thorac Imaging. 2019;34(3):202–13.PubMedCrossRef
16.
Ma J, Song Y, Tian X, Hua Y, Zhang R, Wu J. Survey on deep learning for pulmonary medical imaging. Front Med. 2019;14(4):1–20.
17.
Chassagnon G, Vakalopolou M, Paragios N, Revel MP. Deep learning: definition and perspectives for thoracic imaging. Eur Radiol. 2019;30:1–10.
18.
Williams LH, Drew T. What do we know about volumetric medical image interpretation?: A review of the basic science and medical image perception literatures. Cogn Res Princ Implications. 2019;4:1–24.
19.
Kumar R, Wang W, Kumar J, Yang T, Khan A, Ali W, et al. An integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals. Comput Med Imaging Graph. 2021;87:101812.PubMedCrossRef
20.
Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, et al. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021;83:9–24.PubMedCrossRef
21.
Fourcade A, Khonsari RH. Deep learning in medical image analysis: A third eye for doctors. J Stomatology Oral Maxillofac Surg. 2019;120(4):279–88.CrossRef
22.
Myszczynska MA, Ojamies PN, Lacoste AM, Neil D, Saffari A, Mead R, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16(8):440–56.PubMedCrossRef
23.
Chang R, Qi S, Wu Y, Yue Y, Zhang X, Guan Y, et al. Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer. Transl Oncol. 2023;35:101719.PubMedPubMedCentralCrossRef
24.
Qi S, Zuo Y, Chang R, Huang K, Liu J, Zhang Z. Using CT radiomic features based on machine learning models to subtype adrenal adenoma. BMC Cancer. 2023;23(1):1–12.CrossRef
25.
Chang R, Qi S, Zuo Y, Yue Y, Zhang X, Guan Y, et al. Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined? Front Oncol. 2022;12:915835.PubMedPubMedCentralCrossRef
26.
Xu M, Qi S, Yue Y, Teng Y, Xu L, Yao Y, et al. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset. Biomed Eng Online. 2019;18:1–21.CrossRef
27.
28.
Srivastava A, Jain S, Miranda R, Patil S, Pandya S, Kotecha K. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Comput Sci. 2021;7:e369.PubMedPubMedCentralCrossRef
29.
30.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway: IEEE; 2016. pp. 770–8.
31.
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, et al. Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc. 2020;27(3):457–70.PubMedCrossRef
32.
Wu Y, Qi S, Feng J, Chang R, Pang H, Hou J, et al. Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology. Biocybernetics Biomed Eng. 2023;43(3):568–85.CrossRef
33.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25.
34.
35.
Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.PubMedCrossRef
36.
37.
Wu Y, Du R, Feng J, Qi S, Pang H, Xia S, et al. Deep CNN for COPD identification by Multi-View snapshot integration of 3D airway tree and lung field. Biomed Signal Process Control. 2023;79:104162.CrossRef
38.
Sun J, Liao X, Yan Y, Zhang X, Sun J, Tan W, et al. Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach. Eur Radiol. 2022;32(8):5319–29.PubMedCrossRef
39.
Chen S, Duan J, Wang H, Wang R, Li J, Qi M, et al. Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5. Comput Biol Med. 2022;150:106120.PubMedCrossRef
40.
Chen S, Duan J, Zhang N, Qi M, Li J, Wang H, et al. MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images. Comput Biol Med. 2023;165:107471.PubMedCrossRef
41.
Campo MI, Pascau J, Estépar RSJ. Emphysema quantification on simulated X-rays through deep learning techniques. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Piscataway: IEEE; 2018. pp. 273–6.
42.
Resten A, Maitre S, Musset D. CT imaging of peripheral pulmonary vessel disease. Eur Radiol. 2005;15:2045–56.PubMedCrossRef
43.
Pang H, Wu Y, Qi S, Li C, Shen J, Yue Y, et al. A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images. Comput Biol Med. 2022;147:105792.PubMedCrossRef
44.
Qi Q, Qi S, Wu Y, Li C, Tian B, Xia S, et al. Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images. Comput Biol Med. 2022;141:105182.PubMedCrossRef
45.
Greffier J, Hamard A, Pereira F, Barrau C, Pasquier H, Beregi JP, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol. 2020;30:3951–9.PubMedCrossRef
46.
Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology. 2023;306(3):e221257.PubMedCrossRef
47.
Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 2018;28(2):582–91.PubMedCrossRef
48.
Liu MZ, Mutasa S, Chang P, Siddique M, Jambawalikar S, Ha R. A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database. Magn Reson Imaging. 2020;73:148–51.PubMedPubMedCentralCrossRef
49.
Liu Y, Chen A, Shi H, Huang S, Zheng W, Liu Z, et al. CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy. Comput Med Imaging Graph. 2021;91:101953.PubMedCrossRef
50.
Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, et al. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med Phys. 2019;46(8):3565–81.PubMedPubMedCentralCrossRef
51.
Wu H, Jiang X, Jia F. UC-GAN for MR to CT image synthesis. In: Artificial Intelligence in Radiation Therapy: First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 1. New York: Springer; 2019. pp. 146–53.
52.
Wu Y, Qi S, Sun Y, Xia S, Yao Y, Qian W. A vision transformer for emphysema classification using CT images. Phys Med Biol. 2021;66(24):245016.CrossRef
53.
Pang H, Qi S, Wu Y, Wang M, Li C, Sun Y, et al. NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation. Comput Methods Prog Biomed. 2023;231:107389.CrossRef
54.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations (ICLR); 2021. [Online]. Available: https://​openreview.​net/​forum?​id=​YicbFdNTTy.
55.
Carbonneau MA, Cheplygina V, Granger E, Gagnon G. Multiple instance learning: A survey of problem characteristics and applications. Pattern Recogn. 2018;77:329–53.CrossRef
56.
Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda. Respir Med. 2020;106093.
57.
Exarchos KP, Aggelopoulou A, Oikonomou A, Biniskou T, Beli V, Antoniadou E, et al. Review of artificial intelligence techniques in chronic obstructive lung disease. IEEE J Biomed Health Inf. 2021;26(5):2331–8.CrossRef
58.
Estépar RSJ. Artificial intelligence in COPD: New venues to study a complex disease. Barcelona Respir Netw Rev. 2020;6(2):144.
59.
Wu Y, Zhao S, Qi S, Feng J, Pang H, Chang R, et al. Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images. Artif Intell Med. 2023;143:102637.PubMedCrossRef
60.
Wu Y, Qi S, Wang M, Zhao S, Pang H, Xu J, et al. Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images. Med Biol Eng Comput. 2023;61(10):2649–63.PubMedCrossRef
61.
Madani A, De Maertelaer V, Zanen J, Gevenois PA. Pulmonary emphysema: radiation dose and section thickness at multidetector CT quantification-comparison with macroscopic and microscopic morphometry. Radiology. 2007;243(1):250–7.PubMedCrossRef
62.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.PubMedCrossRef
63.
Yang K, Yang Y, Kang Y, Liang Z, Wang F, Li Q, et al. The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study. Clin Radiol. 2022;77(6):e466–72.PubMedCrossRef
64.
Mets OM, Schmidt M, Buckens CF, Gondrie MJ, Isgum I, Oudkerk M, et al. Diagnosis of chronic obstructive pulmonary disease in lung cancer screening computed tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening. Respir Res. 2013;14:1–8.CrossRef
65.
Ying J, Dutta J, Guo N, Xia L, Sitek A, Li Q. Gold classification of COPDGene cohort based on deep learning. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE; 2016. pp. 2474–8.
66.
González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med. 2018;197(2):193–203.PubMedPubMedCentralCrossRef
67.
Cheplygina V, Pena IP, Pedersen JH, Lynch DA, Sørensen L, de Bruijne M. Transfer learning for multicenter classification of chronic obstructive pulmonary disease. IEEE journal of biomedical and health informatics. 2017;22(5):1486–96.PubMedPubMedCentralCrossRef
68.
Sathiya S, Priyanka G, Jeyanthi S. Detection of chronic obstructive pulmonary disease in computer aided diagnosis system with CNN classification. Int J Pure Appl Math. 2018;119(12):13815–1382.
69.
Singla S, Gong M, Riley C, Sciurba F, Batmanghelich K. Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach. Med Phys. 2021;48(3):1168–81.PubMedCrossRef
70.
Xu C, Qi S, Feng J, Xia S, Kang Y, Yao Y, et al. DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images. Phys Med Biol. 2020;65(14):145011.PubMedCrossRef
71.
Du R, Qi S, Feng J, Xia S, Kang Y, Qian W, et al. Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN. IEEE Access. 2020;8:38907–19.CrossRef
72.
Tang LY, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. Lancet Digit Health. 2020;2(5):e259–67.PubMedCrossRef
73.
Ho TT, Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, et al. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep. 2021;11(1):34.PubMedPubMedCentralCrossRef
74.
Hasenstab KA, Yuan N, Retson T, Conrad DJ, Kligerman S, Lynch DA, et al. Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network. Radiol Cardiothorac Imaging. 2021;3(2):e200477.PubMedPubMedCentralCrossRef
75.
Chen L, Feng Q, Yin X, Min X, Shi L, Yang D, et al. A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images. IEEE J Biomed Health Informa. 2022;26(12):6058–69.CrossRef
76.
Li Z, Liu L, Zhang Z, Yang X, Li X, Gao Y, et al. A novel CT-based radiomics features analysis for identification and severity staging of COPD. Acad Radiol. 2022;29(5):663–73.PubMedCrossRef
77.
Sun P, Wang D, Mok VC, Shi L. Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading. IEEE Access. 2019;7:102010–20.CrossRef
78.
Zhang L, Jiang B, Wisselink HJ, Vliegenthart R, Xie X. COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images. Br J Radiol. 2022;95(1133):20210637.PubMedPubMedCentralCrossRef
79.
Li Z, Huang K, Liu L, Zhang Z. Early detection of COPD based on graph convolutional network and small and weakly labeled data. Med Biol Eng Comput. 2022;60(8):2321–33.PubMedPubMedCentralCrossRef
80.
Yang Y, Li W, Guo Y, Zeng N, Wang S, Chen Z, et al. Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier. Math Biosci Eng. 2022;19(8):7826–55.PubMedCrossRef
81.
Yang Y, Wang S, Zeng N, Duan W, Chen Z, Liu Y, et al. Lung radiomics features selection for COPD stage classification based on auto-metric graph neural network. Diagnostics. 2022;12(10):2274.PubMedPubMedCentralCrossRef
82.
Almeida SD, Lüth CT, Norajitra T, Wald T, Nolden M, Jäger PF, et al. cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. New York: Springer; 2023. pp. 33–43.
83.
Karch A, Vogelmeier C, Welte T, Bals R, Kauczor HU, Biederer J, et al. The German COPD cohort COSYCONET: aims, methods and descriptive analysis of the study population at baseline. Respir Med. 2016;114:27–37.PubMedCrossRef
84.
Xue M, Jia S, Chen L, Huang H, Yu L, Zhu W. CT-based COPD identification using multiple instance learning with two-stage attention. Comput Methods Prog Biomed. 2023;230:107356.CrossRef
85.
Zhou HY, Yu Y, Wang C, Zhang S, Gao Y, Pan J, et al. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat Biomed Eng. 2023;7(6):743–55.
86.
Amudala Puchakayala PR, Sthanam VL, Nakhmani A, Chaudhary MF, Kizhakke Puliyakote A, Reinhardt JM, et al. Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans. Radiology. 2023;307(5):e222998.PubMedCrossRef
87.
Yu K, Sun L, Chen J, Reynolds M, Chaudhary T, Batmanghelich K. DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images. Med Image Anal. 2024;92:103062.
88.
Martini K, Frauenfelder T. Advances in imaging for lung emphysema. Ann Transl Med. 2020;8(21):1467.
89.
Coxson HO, Rogers RM, Whittall KP, D’YACHKOVA Y, Pare PD, Sciurba FC, et al. A quantification of the lung surface area in emphysema using computed tomography. Am J Respir Crit Care Med. 1999;159(3):851–6.PubMedCrossRef
90.
Gangeh MJ, Sørensen L, Shaker SB, Kamel MS, De Bruijne M, Loog M. A texton-based approach for the classification of lung parenchyma in CT images. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III 13. New York: Springer; 2010. pp. 595–602.
91.
Sorensen L, Shaker SB, De Bruijne M. Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans Med Imaging. 2010;29(2):559–69.PubMedCrossRef
92.
Zulueta-Coarasa T, Kurugol S, Ross JC, Washko GG, Estépar RSJ. Emphysema classification based on embedded probabilistic PCA. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway: IEEE; 2013. pp. 3969–72.
93.
Karabulut EM, Ibrikci T. Emphysema discrimination from raw HRCT images by convolutional neural networks. In: 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). Piscataway: IEEE; 2015. pp. 705–8.
94.
Peng L, Lin L, Hu H, Ling X, Wang D, Han X, et al. Joint weber-based rotation invariant uniform local ternary pattern for classification of pulmonary emphysema in CT images. In: 2017 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE; 2017. pp. 2050–4.
95.
Bortsova G, Dubost F, Ørting S, Katramados I, Hogeweg L, Thomsen L, et al. Deep learning from label proportions for emphysema quantification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. New York: Springer; 2018. pp. 768–76.
96.
Bermejo-Peláez D, Estepar RSJ, Ledesma-Carbayo MJ. Emphysema classification using a multi-view convolutional network. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Piscataway: IEEE; 2018. pp. 519–22.
97.
Bermejo-Peláez D, Okajima Y, Washko GR, Ledesma-Carbayo MJ, Estépar RSJ. A SR-NET 3D-to-2D Architecture For Paraseptal Emphysema Segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Piscataway: IEEE; 2019. pp. 303–6.
98.
Peng L, Lin L, Hu H, Li H, Chen Q, Ling X, et al. Classification and quantification of emphysema using a multi-scale residual network. IEEE J Biomed Health Inform. 2019;23(6):2526–36.PubMedCrossRef
99.
Peng L, Lin L, Hu H, Zhang Y, Li H, Iwamoto Y, et al. Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations. IEEE J Biomed Health Inform. 2019;24(8):2327–36.PubMedCrossRef
100.
Li H, Mukundan R. Robust Texture Features For Emphysema Classification In CT Images. In: 2020 28th European Signal Processing Conference (EUSIPCO). Piscataway: IEEE; 2020. pp. 1220–4.
101.
Ørting SN, Petersen J, Thomsen LH, Wille MM, De Bruijne M. Detecting emphysema with multiple instance learning. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). Piscataway: IEEE; 2018. pp. 510–3.
102.
Humphries SM, Notary AM, Centeno JP, Strand MJ, Crapo JD, Silverman EK, et al. Deep learning enables automatic classification of emphysema pattern at CT. Radiology. 2020;294(2):434–44.PubMedCrossRef
103.
Mondal S, Sadhu AK, Dutta PK. Automated diagnosis of pulmonary emphysema using multi-objective binary thresholding and hybrid classification. Biomed Signal Process Control. 2021;69:102886.CrossRef
104.
Nava R, Olveres J, Kybic J, Escalante B, Cristóbal G. Feature ensemble for quantitative analysis of emphysema in CT imaging. In: 2015 E-Health and Bioengineering Conference (EHB). IEEE. pp. 1–4.
105.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. pp. 2818–26.
106.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 4510–20.
107.
Yang J, Angelini ED, Balte PP, Hoffman EA, Austin JH, Smith BM, et al. Novel subtypes of pulmonary emphysema based on spatially-informed lung texture learning: the multi-ethnic study of atherosclerosis (MESA) COPD Study. IEEE Trans Med Imaging. 2021;40(12):3652–62.PubMedPubMedCentralCrossRef
108.
Angelini E, Sun Y, Yang J, Balte P, Hoffman E, Allen N, et al. Longitudinal Progression of Machine-Learned Quantitative Emphysema Subtypes. The Mesa COPD Study. In: D28. COPD: FROM PATHOGENESIS TO PROGNOSIS. American Thoracic Society; 2020. p. A6420.
109.
Hackx M, Bankier AA, Gevenois PA. Chronic obstructive pulmonary disease: CT quantification of airways disease. Radiology. 2012;265(1):34–48.PubMedCrossRef
110.
Jin D, Harrison AP, Zhang L, Yan K, Wang Y, Cai J, et al. Artificial intelligence in radiology. In: Artificial Intelligence in Medicine. New York: Elsevier; 2021. pp. 265–89.
111.
Saha PK, Nadeem SA, Comellas AP. A survey on artificial intelligence in pulmonary imaging. Wiley Interdiscip Rev Data Min Knowl Disc. 2023;13(6):e1510.CrossRef
112.
Little S, Sproule M, Cowan M, Macleod K, Robertson M, Love J, et al. High resolution computed tomographic assessment of airway wall thickness in chronic asthma: reproducibility and relationship with lung function and severity. Thorax. 2002;57(3):247–53.PubMedPubMedCentralCrossRef
113.
Kocak B, Ates E, Durmaz ES, Ulusan MB, Kilickesmez O. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol. 2019;29:4765–75.PubMedCrossRef
114.
Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med. 2022;151:106241.
115.
Cheng G, Wu X, Xiang W, Guo C, Ji H, He L. Segmentation of the airway tree from chest CT using tiny atrous convolutional network. IEEE Access. 2021;9:33583–94.CrossRef
116.
Zheng H, Qin Y, Gu Y, Xie F, Yang J, Sun J, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation. IEEE Trans Med Imaging. 2021;40(9):2452–62.PubMedCrossRef
117.
118.
119.
Nan Y, Del Ser J, Tang Z, Tang P, Xing X, Fang Y, et al. Fuzzy attention neural network to tackle discontinuity in airway segmentation. IEEE Trans Neural Netw Learn Syst. 2023;35(6):7391–404.
120.
121.
Charbonnier JP, Van Rikxoort EM, Setio AA, Schaefer-Prokop CM, van Ginneken B, Ciompi F. Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal. 2017;36:52–60.PubMedCrossRef
122.
Yun J, Park J, Yu D, Yi J, Lee M, Park HJ, et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Med Image Anal. 2019;51:13–20.PubMedCrossRef
123.
124.
Qin Y, Chen M, Zheng H, Gu Y, Shen M, Yang J, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention. New York: Springer; 2019. pp. 212–20.
125.
Nadeem SA, Hoffman EA, Sieren JC, Comellas AP, Bhatt SP, Barjaktarevic IZ, et al. A CT-based automated algorithm for airway segmentation using freeze-and-grow propagation and deep learning. IEEE Trans Med Imaging. 2020;40(1):405–18.PubMedCrossRef
126.
Selvan R, Kipf T, Welling M, Juarez AGU, Pedersen JH, Petersen J, et al. Graph refinement based airway extraction using mean-field networks and graph neural networks. Med Image Anal. 2020;64:101751.PubMedCrossRef
127.
128.
Guo J, Fu R, Pan L, Zheng S, Huang L, Zheng B, et al. Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing. Comput Methods Prog Biomed. 2022;215:106610.CrossRef
129.
Heitz A, Weinzorn J, Noblet V, Naegel B, Charnoz A, Heitz F, et al. Lubrav: a new framework for the segmentation of the lung’s tubular structures. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). Piscataway: IEEE; 2021. pp. 948–52.
130.
Qin Y, Zheng H, Gu Y, Huang X, Yang J, Wang L, et al. Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT. IEEE Trans Med Imaging. 2021;40(6):1603–17.PubMedCrossRef
131.
132.
133.
Wu Y, Zhang M, Yu W, Zheng H, Xu J, Gu Y. LTSP: long-term slice propagation for accurate airway segmentation. Int J CARS. 2022;17(5):857–65.CrossRef
134.
135.
Park J, Hobbs BD, Crapo JD, Make BJ, Regan EA, Humphries S, et al. Subtyping COPD by using visual and quantitative CT imaging features. Chest. 2020;157(1):47–60.PubMedCrossRef
136.
Castaldi PJ, Boueiz A, Yun J, Estepar RSJ, Ross JC, Washko G, et al. Machine learning characterization of COPD subtypes: insights from the COPDGene study. Chest. 2020;157(5):1147–57.PubMedCrossRef
137.
Tanabe N, Shimizu K, Terada K, Sato S, Suzuki M, Shima H, et al. Central airway and peripheral lung structures in airway disease-dominant COPD. ERJ Open Res. 2021;7(1):00672–2020.
138.
Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo CH. A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators. Sci Rep. 2020;10(1):3118.PubMedPubMedCentralCrossRef
139.
Lanclus M, Clukers J, Van Holsbeke C, Vos W, Leemans G, Holbrechts B, et al. Machine learning algorithms utilizing functional respiratory imaging may predict COPD exacerbations. Acad Radiol. 2019;26(9):1191–9.PubMedCrossRef
140.
Zeng S, Arjomandi M, Tong Y, Liao ZC, Luo G. Developing a machine learning model to predict severe chronic obstructive pulmonary disease exacerbations: retrospective cohort study. J Med Internet Res. 2022;24(1):e28953.PubMedPubMedCentralCrossRef
141.
Young AL, Bragman FJ, Rangelov B, Han MK, Galbán CJ, Lynch DA, et al. Disease progression modeling in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2020;201(3):294–302.PubMedPubMedCentralCrossRef
142.
Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, et al. Artificial intelligence in cardiovascular CT: Current status and future implications. J Cardiovasc Comput Tomogr. 2021;15(6):462–9.PubMedPubMedCentralCrossRef
143.
Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ, et al. Pulmonary artery-vein classification in CT images using deep learning. IEEE Trans Med Imaging. 2018;37(11):2428–40.PubMedPubMedCentralCrossRef
144.
Xu Y, Mao Z, Liu C, Wang B. Pulmonary vessel segmentation via stage-wise convolutional networks with orientation-based region growing optimization. IEEE Access. 2018;6:71296–305.CrossRef
145.
Gu X, Wang J, Zhao J, Li Q. Segmentation and suppression of pulmonary vessels in low-dose chest CT scans. Med Phys. 2019;46(8):3603–14.PubMedCrossRef
146.
Guo W, Gu X, Fang Q, Li Q. Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs. Radiol Phys Technol. 2021;14:6–15.PubMedCrossRef
147.
Tan W, Zhou L, Li X, Yang X, Chen Y, Yang J. Automated vessel segmentation in lung CT and CTA images via deep neural networks. J X-ray Sci Technol. 2021;29(6):1123–37.
148.
Jimenez-Carretero D, Bermejo-Peláez D, Nardelli P, Fraga P, Fraile E, Estépar RSJ, et al. A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. Med Image Anal. 2019;52:144–59.PubMedCrossRef
149.
Cui H, Liu X, Huang N. Pulmonary vessel segmentation based on orthogonal fused U-Net++ of chest CT images. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22. New York: Springer; 2019. pp. 293–300.
150.
Nam JG, Witanto JN, Park SJ, Yoo SJ, Goo JM, Yoon SH. Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps. Eur Radiol. 2021;31(12):9012–21.PubMedPubMedCentralCrossRef
151.
Wu R, Xin Y, Qian J, Dong Y. A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning. Biomed Signal Process Control. 2023;80:104407.CrossRef
152.
Li X, Bala R, Monga V. Robust deep 3D blood vessel segmentation using structural priors. IEEE Trans Image Process. 2022;31:1271–84.PubMedCrossRef
153.
Wang M, Qi S, Wu Y, Sun Y, Chang R, Pang H, et al. CE-NC-VesselSegNet: Supervised by contrast-enhanced CT images but utilized to segment pulmonary vessels from non-contrast-enhanced CT images. Biomed Signal Process Control. 2023;82:104565.CrossRef
154.
Pan L, Li Z, Shen Z, Liu Z, Huang L, Yang M, et al. Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation. Comput Biol Med. 2023;155:106669.PubMedCrossRef
155.
Litchman T, de Margerie-Mellon C, Bankier A, Washko G, Rahaghi F, San Jose Estepar R, et al. Pulmonary Vascular Pruning on Computed Tomography and Risk of Progression of Lung Adenocarcinoma. In: D20. NOT YOUR GRANDFATHER’S CT SCAN: RADIOMICS AND DEEP MACHINE LEARNING FOR PULMONARY NODULES AND LUNG CANCER EVALUATION. American Thoracic Society; 2022. p. A5074.
156.
Patel AR, Hurst JR. Extrapulmonary comorbidities in chronic obstructive pulmonary disease: state of the art. Expert Rev Respir Med. 2011;5(5):647–62.PubMedCrossRef
157.
Machado A, Marques A, Burtin C. Extra-pulmonary manifestations of COPD and the role of pulmonary rehabilitation: a symptom-centered approach. Expert Rev Respir Med. 2021;15(1):131–42.PubMedCrossRef
158.
Chattopadhyay S, Das R. Statistical Validation of Cardiovascular Digital Biomarkers Towards Monitoring the Cardiac Risk in COPD: A Lyfas Case Study. Artif Intell Evol. 2022;3(1):1–16.
159.
Balbirsingh V, Mohammed AS, Turner AM, Newnham M. Cardiovascular disease in chronic obstructive pulmonary disease: a narrative review. Thorax. 2022;77(9):939–45.CrossRef
160.
Franssen FM, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, et al. Personalized medicine for patients with COPD: where are we? Int J Chronic Obstructive Pulm Dis. 2019;14:1465–84.
161.
Wouters EF, Wouters BB, Augustin IM, Houben-Wilke S, Vanfleteren LE, Franssen FM. Personalised pulmonary rehabilitation in COPD. Eur Respir Rev. 2018;27(147).
162.
Bećirović LS, Deumić A, Pokvić LG, Badnjevic A. Aritificial Inteligence Challenges in COPD management: a review. In: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). Piscataway: IEEE; 2021. pp. 1–7.
163.
Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, Beaty TH, et al. Genetic epidemiology of COPD (COPDGene) study design. COPD J Chronic Obstructive Pulm Dis. 2011;7(1):32–43.CrossRef
164.
Castelvecchi D. Can we open the black box of AI? Nat News. 2016;538(7623):20.CrossRef
165.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. Piscataway: IEEE; 2017. pp. 618–26.
166.
Chefer H, Gur S, Wolf L. Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Piscataway: IEEE; 2021. pp. 782–91.
167.
Altan G, Kutlu Y, Allahverdi N. Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE J Biomed Health Inform. 2019;24(5):1344–50.CrossRef
168.
Oh AS, Baraghoshi D, Lynch DA, Ash SY, Crapo JD, Humphries SM, et al. Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study. Radiology. 2022;304(3):672–9.PubMedCrossRef
169.
Chen J, Zhonghui X, Sun L, Yu K, Hersh CP, Boueiz A, et al. Deep Learning Integration of Chest CT Imaging and Gene Expression Identifies Novel Aspects of COPD. Chronic Obstr Pulm Dis. 2023;10(4):355–68.
170.
Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE. 2021;109(5):820–38.CrossRef
171.
Gayle AV, Quint JK, Fuertes EI. Understanding the relationships between environmental factors and exacerbations of COPD. Exp Rev Respir Med. 2021;15(1):39–50.CrossRef
172.
Bourbeau J, Collet JP, Schwartzman K, Ducruet T, Nault D, Bradley C. Economic benefits of self-management education in COPD. Chest. 2006;130(6):1704–11.PubMedCrossRef
Metadata
Title
Artificial intelligence in COPD CT images: identification, staging, and quantitation
Authors
Yanan Wu
Shuyue Xia
Zhenyu Liang
Rongchang Chen
Shouliang Qi
Publication date
22-08-2024
Publisher
BioMed Central
Published in
Respiratory Research / Issue 1/2024
Electronic ISSN: 1465-993X
DOI
https://doi.org/10.1186/s12931-024-02913-z

Other articles of this Issue 1/2024

Respiratory Research 1/2024 Go to the issue

Research

Fine particulate matter 2.5 induces susceptibility to Pseudomonas aeruginosa infection via expansion of PD-L1high neutrophils in mice

Research

YAP/TAZ activation mediates PQ-induced lung fibrosis by sustaining senescent pulmonary epithelial cells

Research

Features which discriminate between tuberculosis and haematologic malignancy as the cause of pleural effusions with high adenosine deaminase

Research

Exertional breathlessness related to medical conditions in middle-aged people: the population-based SCAPIS study of more than 25,000 men and women

Research

Downregulation of HMGCS2 mediated AECIIs lipid metabolic alteration promotes pulmonary fibrosis by activating fibroblasts

Research

Radiomics based on HRCT can predict RP-ILD and mortality in anti-MDA5 + dermatomyositis patients: a multi-center retrospective study

2024 ERS Congress

AI improves spirometry in primary care

09-09-2024 Artificial Intelligence News

An AI support tool could aid clinicians using spirometry to accurately predict respiratory diagnoses, particularly chronic obstructive pulmonary disease.

Read more

Benralizumab new treatment for asthma, COPD exacerbations

10-09-2024 Obstructive Lung Diseases News

Air pollution worsens respiratory conditions

10-09-2024 Air Pollutants News

Bexotegrast shows potential for disease modification in IPF

09-09-2024 Idiopathic Pulmonary Fibrosis News

» View more highlights on the ERS 2024 Congress hub page

SPONSORED

Targeting COPD with type 2 inflammation: emerging biologic landscape

Live Webinar | 03-12-2024 | 19:00 (CET)

LIVE: Tuesday, 3 December 2024, 19:00-20:30 (CET)

Join us for an insightful session tailored for pulmonologists and healthcare professionals eager to enhance their understanding of COPD and the latest advances in biologic therapies. 

Sponsored by:
  • Sanofi
Developed by: Springer Healthcare
Reserve your place

A quick guide to ECGs

Electrocardiography Training Course

Improve your ECG interpretation skills with this comprehensive, rapid, interactive course. Expert advice provides detailed feedback as you work through 50 ECGs covering the most common cardiac presentations to ensure your practice stays up to date. 

PD Dr. Carsten W. Israel
Developed by: Springer Medizin
Start the cases
Image Credits