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04-09-2024 | Obstructive Sleep Apnea | REVIEW Free for a limited time

Artificial Intelligence for Diagnosis of Obstructive Sleep Apnea

Authors: Jasmine May, Roneil G. Malkani

Published in: Current Pulmonology Reports

Abstract

Purpose of Review

This review focuses on the different methodologies and artificial intelligence (AI) technologies being utilized to improve rapid diagnosis of sleep disordered breathing (SDB).

Recent Findings

Many recent studies have examined the application of AI neural networks on imaging techniques, single lead diagnostics, and multi-lead technologies to improve screening and automated diagnosis of SDB. While several techniques are better at identifying moderate or severe sleep apnea, the ability to detect mild cases of sleep apnea still lags. A major limitation of these studies is the small and homogenous patient populations in training datasets, limiting generalization and thus application.

Summary

Application of AI in the diagnosis of SDB is promising and great advances have been made. Further research is needed to develop more generalizable techniques before widespread application.

American Academy of Sleep Medicine. International classification of sleep disorders. 3rd ed. Darien: American Academy of Sleep Medicine; 2014.

Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006–14. https://doi.org/10.1093/aje/kws342.CrossRefPubMedPubMedCentral

Redline S, Sotres-Alvarez D, Loredo J, Hall M, Patel SR, Ramos A, et al. Sleep-disordered breathing in Hispanic/Latino individuals of diverse backgrounds. The Hispanic Community Health Study/Study of Latinos. Am J Respir Crit Care Med. 2014;189(3):335–44. https://doi.org/10.1164/rccm.201309-1735OC.CrossRefPubMedPubMedCentral

Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7(8):687–98. https://doi.org/10.1016/S2213-2600(19)30198-5.CrossRefPubMedPubMedCentral

Somers VK, White DP, Amin R, Abraham WT, Costa F, Culebras A, et al. Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation Scientific Statement from the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing. J Am Coll Cardiol. 2008;52(8):686–717. https://doi.org/10.1016/j.jacc.2008.05.002.CrossRefPubMed

Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342(19):1378–84. https://doi.org/10.1056/NEJM200005113421901.CrossRefPubMed

Sharma S, Culebras A. Sleep apnoea and stroke. Stroke Vasc Neurol. 2016;1(4):185–91. https://doi.org/10.1136/svn-2016-000038.CrossRefPubMedPubMedCentral

Yaffe K, Laffan AM, Harrison SL, Redline S, Spira AP, Ensrud KE, et al. Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. JAMA. 2011;306(6):613–9. https://doi.org/10.1001/jama.2011.1115.CrossRefPubMedPubMedCentral

Kim HC, Young T, Matthews CG, Weber SM, Woodward AR, Palta M. Sleep-disordered breathing and neuropsychological deficits. A population-based study. Am J Respir Crit Care Med. 1997;156(6):1813–9. https://doi.org/10.1164/ajrccm.156.6.9610026.CrossRefPubMed

10.

Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071–8.PubMedPubMedCentral

11.

Marshall NS, Wong KK, Liu PY, Cullen SR, Knuiman MW, Grunstein RR. Sleep apnea as an independent risk factor for all-cause mortality: the Busselton Health Study. Sleep. 2008;31(8):1079–85.PubMedPubMedCentral

12.

Ayas NT, FitzGerald JM, Fleetham JA, White DP, Schulzer M, Ryan CF, et al. Cost-effectiveness of continuous positive airway pressure therapy for moderate to severe obstructive sleep apnea/hypopnea. Arch Intern Med. 2006;166(9):977–84. https://doi.org/10.1001/archinte.166.9.977.CrossRefPubMed

13.

Frost and Sullivan. Hidden health crisis costing America billions. Underdiagnosing and undertreating obstructive sleep apnea draining healthcare system. American Academy of Sleep Medicine. 2016. www.aasm.org.

14.

Renganathan V. Overview of artificial neural network models in the biomedical domain. Bratisl Lek Listy. 2019;120(7):536–40. https://doi.org/10.4149/BLL_2019_087.CrossRefPubMed

15.

Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021;14(1):86–93. https://doi.org/10.1111/cts.12884.CrossRefPubMed

16.

Brennan HL, Kirby SD. Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2022;51(1):16. https://doi.org/10.1186/s40463-022-00566-w.CrossRefPubMedPubMedCentral

17.

Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digit Health. 2023;2(6):e0000278. https://doi.org/10.1371/journal.pdig.0000278.CrossRefPubMedPubMedCentral

18.

Tiribelli S, Monnot A, Shah SFH, Arora A, Toong PJ, Kong S. Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health. Am J Public Health. 2023;113(5):577–84. https://doi.org/10.2105/ajph.2023.307225.CrossRefPubMed

19.

Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective. Sleep Breath. 2023;27(1):39–55. https://doi.org/10.1007/s11325-022-02592-4.CrossRefPubMed

20.

Westbrook PR, Levendowski DJ, Cvetinovic M, Zavora T, Velimirovic V, Henninger D, et al. Description and validation of the apnea risk evaluation system: a novel method to diagnose sleep apnea-hypopnea in the home. Chest. 2005;128(4):2166–75. https://doi.org/10.1378/chest.128.4.2166.CrossRefPubMed

21.

Ioachimescu OC, Dholakia SA, Venkateshiah SB, Fields B, Samarghandi A, Anand N, et al. Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea. J Investig Med. 2020;68(8):1370–8. https://doi.org/10.1136/jim-2020-001448.CrossRefPubMedPubMedCentral

22.

Hung CJ, Kang BH, Lin YS, Su HH. Comparison of a home sleep test with in-laboratory polysomnography in the diagnosis of obstructive sleep apnea syndrome. J Chin Med Assoc. 2022;85(7):788–92. https://doi.org/10.1097/jcma.0000000000000741.CrossRefPubMed

23.

Ioachimescu OC, Allam JS, Samarghandi A, Anand N, Fields BG, Dholakia SA, et al. Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort. J Clin Sleep Med. 2020;16(10):1663–74. https://doi.org/10.5664/jcsm.8620.CrossRefPubMedPubMedCentral

24.

Zhang L, Yan YR, Li SQ, Li HP, Lin YN, Li N, et al. Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study. BMJ Open. 2021;11(9):e048482. https://doi.org/10.1136/bmjopen-2020-048482.CrossRefPubMedPubMedCentral

25.

Kuan YC, Hong CT, Chen PC, Liu WT, Chung CC. Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index. Math Biosci Eng. 2022;19(11):11409–21. https://doi.org/10.3934/mbe.2022532.CrossRefPubMed

26.

Hsu YC, Wang JD, Huang PH, Chien YW, Chiu CJ, Lin CY. Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature. J Sleep Res. 2022;31(2):e13487. https://doi.org/10.1111/jsr.13487.CrossRefPubMed

27.

Holfinger SJ, Lyons MM, Keenan BT, Mazzotti DR, Mindel J, Maislin G, et al. Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples. Chest. 2022;161(3):807–17. https://doi.org/10.1016/j.chest.2021.10.023.CrossRefPubMed

28.

Huang WC, Lee PL, Liu YT, Chiang AA, Lai F. Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample. Sleep. 2020;43(7). https://doi.org/10.1093/sleep/zsz295.

29.

Juang CF, Wen CY, Chang KM, Chen YH, Wu MF, Huang WC. Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome. Sleep Med. 2021;85:280–90. https://doi.org/10.1016/j.sleep.2021.07.012.CrossRefPubMed

30.

He S, Su H, Li Y, Xu W, Wang X, Han D. Detecting obstructive sleep apnea by craniofacial image-based deep learning. Sleep Breath. 2022;26(4):1885–95. https://doi.org/10.1007/s11325-022-02571-9.CrossRefPubMed

31.

Hanif U, Leary E, Schneider L, Paulsen R, Morse AM, Blackman A, et al. Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans. IEEE J Biomed Health Inform. 2021;25(11):4185–94. https://doi.org/10.1109/jbhi.2021.3078127.CrossRefPubMed

32.

Tsuiki S, Nagaoka T, Fukuda T, Sakamoto Y, Almeida FR, Nakayama H, et al. Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study. Sleep Breath. 2021;25(4):2297–305. https://doi.org/10.1007/s11325-021-02301-7.CrossRefPubMedPubMedCentral

33.

Ryu S, Kim JH, Yu H, Jung HD, Chang SW, Park JJ, et al. Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach. Comput Methods Programs Biomed. 2021;208:106243. https://doi.org/10.1016/j.cmpb.2021.106243.CrossRefPubMed

34.

Monna F, Ben Messaoud R, Navarro N, Baillieul S, Sanchez L, Loiodice C, et al. Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans. Sleep Med. 2022;95:76–83. https://doi.org/10.1016/j.sleep.2022.04.019.CrossRefPubMed

35.

Hajipour F, Jozani MJ, Moussavi Z. A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea. Med Biol Eng Comput. 2020;58(10):2517–29. https://doi.org/10.1007/s11517-020-02206-9.CrossRefPubMed

36.

Korkalainen H, Aakko J, Nikkonen S, Kainulainen S, Leino A, Duce B, et al. Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea. IEEE J Biomed Health Inform. 2020;24(7):2073–81. https://doi.org/10.1109/jbhi.2019.2951346.CrossRefPubMed

37.

Huttunen R, Leppänen T, Duce B, Oksenberg A, Myllymaa S, Töyräs J, et al. Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography. Sleep. 2021;44(10). https://doi.org/10.1093/sleep/zsab142.

38.

Van Steenkiste T, Groenendaal W, Dreesen P, Lee S, Klerkx S, de Francisco R, et al. Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning. IEEE J Biomed Health Inform. 2020;24(9):2589–98. https://doi.org/10.1109/jbhi.2020.2967872.CrossRefPubMed

39.

Nassi TE, Ganglberger W, Sun H, Bucklin AA, Biswal S, van Putten M, et al. Automated Scoring of Respiratory Events in Sleep With a Single Effort Belt and Deep Neural Networks. IEEE Trans Biomed Eng. 2022;69(6):2094–104. https://doi.org/10.1109/tbme.2021.3136753.CrossRefPubMedPubMedCentral

40.

ElMoaqet H, Eid M, Glos M, Ryalat M, Penzel T. Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors (Basel). 2020;20(18). https://doi.org/10.3390/s20185037.

41.

Mostafa SS, Baptista D, Ravelo-García AG, Juliá-Serdá G, Morgado-Dias F. Greedy based convolutional neural network optimization for detecting apnea. Comput Methods Programs Biomed. 2020;197:105640. https://doi.org/10.1016/j.cmpb.2020.105640.CrossRefPubMed

42.

Vaquerizo-Villar F, Álvarez D, Gutiérrez-Tobal GC, Arroyo-Domingo CA, Del Campo F, Hornero R. Deep-learning model based on convolutional neural networks to classify apnea-hypopnea events from the oximetry signal. Adv Exp Med Biol. 2022;1384:255–64. https://doi.org/10.1007/978-3-031-06413-5_15.CrossRefPubMed

43.

Leino A, Nikkonen S, Kainulainen S, Korkalainen H, Töyräs J, Myllymaa S, et al. Neural network analysis of nocturnal SpO(2) signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease. Sleep Med. 2021;79:71–8. https://doi.org/10.1016/j.sleep.2020.12.032.CrossRefPubMed

44.

Sharma P, Jalali A, Majmudar M, Rajput KS, Selvaraj N. Deep-learning based sleep apnea detection using SpO2 and pulse rate. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:2611–4. https://doi.org/10.1109/embc48229.2022.9871295.CrossRefPubMed

45.

Penzel T, Moody G, Mark R, Goldberger A, Peter J. The Apnea-ECG Database. Comput Cardiol. 2000;27:255–8.

46.

Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20. https://doi.org/10.1161/01.cir.101.23.e215.CrossRefPubMed

47.

Chang HC, Wu HT, Huang PC, Ma HP, Lo YL, Huang YH. Portable sleep apnea syndrome screening and event detection using long short-term memory recurrent neural network. Sensors (Basel). 2020;20(21). https://doi.org/10.3390/s20216067.

48.

Bernardini A, Brunello A, Gigli GL, Montanari A, Saccomanno N. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning. Artif Intell Med. 2021;118:102133. https://doi.org/10.1016/j.artmed.2021.102133.CrossRefPubMed

49.

Mashrur FR, Islam MS, Saha DK, Islam SMR, Moni MA. SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals. Comput Biol Med. 2021;134:104532. https://doi.org/10.1016/j.compbiomed.2021.104532.CrossRefPubMed

50.

Nasifoglu H, Erogul O. Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks. Physiol Meas. 2021;42(6). https://doi.org/10.1088/1361-6579/ac0a9c.

51.

Iwasaki A, Fujiwara K, Nakayama C, Sumi Y, Kano M, Nagamoto T, et al. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. Clin Neurophysiol. 2022;139:80–9. https://doi.org/10.1016/j.clinph.2022.04.012.CrossRefPubMed

52.

Paul T, Hassan O, Alaboud K, Islam H, Rana MKZ, Islam SK, et al. ECG and SpO(2) signal-based real-time sleep apnea detection using feed-forward artificial neural network. AMIA Annu Symp Proc. 2022;2022:379–85.

53.

Simegn GL, Nemomssa HD, Ayalew MP. Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO(2) signals. J Med Eng Technol. 2022;46(2):148–57. https://doi.org/10.1080/03091902.2022.2026503.CrossRefPubMed

54.

Li Z, Li Y, Zhao G, Zhang X, Xu W, Han D. A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index. Sleep Breath. 2021;25(4):2065–72. https://doi.org/10.1007/s11325-021-02302-6.CrossRefPubMed

55.

Prucnal MA, Polak AG. Analysis of features extracted from EEG epochs by discrete wavelet decomposition and hilbert transform for sleep apnea detection. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:287–90. https://doi.org/10.1109/EMBC.2018.8512201.CrossRefPubMed

56.

Taghizadegan Y, Jafarnia Dabanloo N, Maghooli K, Sheikhani A. Prediction of obstructive sleep apnea using ensemble of recurrence plot convolutional neural networks (RPCNNs) from polysomnography signals. Med Hypotheses. 2021;154:110659. https://doi.org/10.1016/j.mehy.2021.110659.CrossRefPubMed

57.

Korkalainen H, Aakko J, Duce B, Kainulainen S, Leino A, Nikkonen S, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11). https://doi.org/10.1093/sleep/zsaa098.

58.

Dietz-Terjung S, Martin AR, Finnsson E, Ágústsson JS, Helgason S, Helgadóttir H, et al. Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography. Sleep Breath. 2021;25(4):1945–52. https://doi.org/10.1007/s11325-021-02316-0.CrossRefPubMedPubMedCentral

59.

Romero HE, Ma N, Brown GJ, Hill EA. Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks. IEEE J Biomed Health Inform. 2022;26(7):2941–50. https://doi.org/10.1109/jbhi.2022.3154719.CrossRefPubMed

60.

Yu Y, Yang Z, You Y, Shan W. FASSNet: fast apnea syndrome screening neural network based on single-lead electrocardiogram for wearable devices. Physiol Meas. 2021;42(8). https://doi.org/10.1088/1361-6579/ac184e.

61.

Mukherjee D, Dhar K, Schwenker F, Sarkar R. Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study. Sensors (Basel). 2021;21(16). https://doi.org/10.3390/s21165425.

62.

Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328(17):1230–5. https://doi.org/10.1056/NEJM199304293281704.CrossRefPubMed

63.

Arnardottir ES, Bjornsdottir E, Olafsdottir KA, Benediktsdottir B, Gislason T. Obstructive sleep apnoea in the general population: highly prevalent but minimal symptoms. Eur Respir J. 2016;47(1):194–202. https://doi.org/10.1183/13993003.01148-2015.CrossRefPubMed

64.

Lee J, Choi JH, Kim S-G. Prevalence and Characteristics of Subjects with Obstructive Sleep Apnea among Adults with Insomnia Disorder. Sleep Medicine Research. 2019;10(2):108–12.CrossRef

65.

Nikkonen S, Korkalainen H, Leino A, Myllymaa S, Duce B, Leppanen T, et al. Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network. IEEE J Biomed Health Inform. 2021;25(8):2917–27. https://doi.org/10.1109/jbhi.2021.3064694.CrossRefPubMed

66.

Ma B, Wu Z, Li S, Benton R, Li D, Huang Y, et al. Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis. BMC Med Inform Decis Mak. 2020;20(Suppl 14):298. https://doi.org/10.1186/s12911-020-01329-1.CrossRefPubMedPubMedCentral

67.

Iwasaki A, Nakayama C, Fujiwara K, Sumi Y, Matsuo M, Kano M, et al. Screening of sleep apnea based on heart rate variability and long short-term memory. Sleep Breath. 2021;25(4):1821–9. https://doi.org/10.1007/s11325-020-02249-0.CrossRefPubMedPubMedCentral

68.

Urtnasan E, Park JU, Joo EY, Lee KJ. Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG. J Korean Med Sci. 2020;35(47):e399. https://doi.org/10.3346/jkms.2020.35.e399.CrossRefPubMedPubMedCentral

69.

Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev. 2021;59:101512. https://doi.org/10.1016/j.smrv.2021.101512.CrossRefPubMed

70.

Tsai CY, Huang HT, Cheng HC, Wang J, Duh PJ, Hsu WH, et al. Screening for obstructive sleep apnea risk by using machine learning approaches and anthropometric features. Sensors (Basel). 2022;22(22). https://doi.org/10.3390/s22228630.

71.

Chang HY, Yeh CY, Lee CT, Lin CC. A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram. Sensors (Basel). 2020; 20(15). https://doi.org/10.3390/s20154157.

72.

Niroshana SMI, Zhu X, Nakamura K, Chen W. A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network. PLoS ONE. 2021;16(4):e0250618. https://doi.org/10.1371/journal.pone.0250618.CrossRefPubMedPubMedCentral

73.

Castillo-Escario Y, Werthen-Brabants L, Groenendaal W, Deschrijver D, Jane R. Convolutional neural networks for apnea detection from smartphone audio signals: effect of window size. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:666–9. https://doi.org/10.1109/embc48229.2022.9871396.CrossRefPubMed

Title: Artificial Intelligence for Diagnosis of Obstructive Sleep Apnea
Authors: Jasmine May
Roneil G. Malkani
Publication date: 04-09-2024
Publisher: Springer US
Keywords: Obstructive Sleep Apnea
Artificial Intelligence
Sleep-Related Breathing Disorders
Polysomnography
General Medicine
Published in: Current Pulmonology Reports
Electronic ISSN: 2199-2428
DOI: https://doi.org/10.1007/s13665-024-00361-0

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Artificial Intelligence for Diagnosis of Obstructive Sleep Apnea

Abstract

Purpose of Review

Recent Findings

Summary

Targeting COPD with type 2 inflammation: emerging biologic landscape

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A quick guide to ECGs

Keynote webinar | Spotlight on modern management of frailty

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Purpose of Review

Recent Findings

Summary

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Targeting COPD with type 2 inflammation: emerging biologic landscape

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