Personalized management of chronic obstructive pulmonary disease using artificial intelligence technologies in primary care

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Chronic obstructive pulmonary disease remains one of the leading causes of morbidity and mortality worldwide, exerting a substantial impact on patients’ quality of life and healthcare systems. Traditional diagnostic and therapeutic approaches, including spirometry, clinical scoring systems, and imaging techniques, have important limitations related to the requirement for active patient participation, high costs, and difficulties in ensuring long-term monitoring. In recent years, artificial intelligence technologies integrated with physiological signal analysis have opened new opportunities for personalized and management of chronic obstructive pulmonary disease, particularly in ambulatory and primary care settings. This review summarizes recent advances in the application of artificial intelligence across four key domains: diagnosis, severity classification, exacerbation prediction, and therapeutic interventions. Special emphasis is placed on multimodal analysis of physiological signals, including respiratory sounds, blood oxygen saturation, electromyographic, and cardiorespiratory parameters. Integration of these data with machine learning and deep learning algorithms has been shown to improve early diagnostic accuracy, enable preclinical prediction of exacerbations, optimize therapeutic interventions, and enhance treatment adherence. Despite these promising developments, substantial barriers to clinical implementation of artificial intelligence remain, including fragmentation and heterogeneity of medical data, limited interpretability of complex models, the need for standardized data acquisition protocols, and the requirement for large-scale multicenter clinical studies. Nevertheless, integration of intelligent systems into the workflow of primary care physicians has the potential to transform chronic obstructive pulmonary disease management by shifting from reactive treatment toward proactive and personalized monitoring, ultimately reducing exacerbation frequency and improving patient quality of life.

作者简介

Alina Sedinkina

Tyumen State Medical University

编辑信件的主要联系方式.
Email: a_asedinkina@mail.ru
ORCID iD: 0009-0005-4514-7692
俄罗斯联邦, Tyumen

Regina Biktasheva

Bashkir State Medical University

Email: regishka519@mail.ru
ORCID iD: 0009-0002-1444-4078
俄罗斯联邦, Ufa

Vazrail Abastov

North Caucasian State Academy

Email: vz.abastov@mail.ru
ORCID iD: 0009-0000-5443-3002
俄罗斯联邦, Cherkessk

Daria Fomina

Kuban State Medical University

Email: angiifomina@mail.ru
ORCID iD: 0009-0000-3918-2735
俄罗斯联邦, Krasnodar

Anna Fedorova

Kuban State Medical University

Email: mofyto@mail.ru
ORCID iD: 0009-0009-8681-2774
俄罗斯联邦, Krasnodar

Andrew Iukov

Kuban State Medical University

Email: maxjer85@gmail.com
ORCID iD: 0009-0001-9657-8696
俄罗斯联邦, Krasnodar

Amina Botasheva

North Caucasian State Academy

Email: amina.botasheva.03@mail.ru
ORCID iD: 0009-0005-6153-6372
俄罗斯联邦, Cherkessk

Nelli Kazaryan

Kuban State Medical University

Email: balykova.nelli@yandex.ru
ORCID iD: 0009-0001-0985-1915
俄罗斯联邦, Krasnodar

Milana Tishchenko

V.I. Vernadsky Crimean Federal University

Email: tish.mila@bk.ru
ORCID iD: 0009-0009-6804-1988
俄罗斯联邦, Simferopol

Alina Grigoryan

Kuban State Medical University

Email: Alina.Asryan11@yandex.ru
ORCID iD: 0009-0000-0803-0376
俄罗斯联邦, Krasnodar

Elizaveta Panfilova

Academician I.P. Pavlov First St. Petersburg State Medical University

Email: lizapanfilova.05@list.ru
ORCID iD: 0009-0009-6763-970X
俄罗斯联邦, Saint Petersburg

Marina Kolesnik

Smolensk City Polyclinic

Email: marinkwe@yandex.ru
ORCID iD: 0009-0005-2776-972X

MD

俄罗斯联邦, Smolensk

Tamara Maskaeva

Bobrov District Hospital

Email: maskaeva.tamara2016@yandex.ru
ORCID iD: 0009-0008-9328-7229

MD

俄罗斯联邦, Bobrov

参考

  1. Mathur S, Singh P. Chronic obstructive pulmonary disease: lifestyle impact. Int J Prev Med. 2024;15:67. doi: 10.4103/ijpvm.ijpvm_297_23 EDN: XGJPXZ
  2. De Ramón Fernández A, Ruiz Fernández D, Gilart Iglesias V, Marcos Jorquera D. Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD). Int J Med Inform. 2022;158:104640. doi: 10.1016/j.ijmedinf.2021.104640 EDN: IFBAFG
  3. Avdeev SN, Leshchenko IV, Aisanov ZR. New concept and algorithm for the management of patients with chronic obstructive pulmonary disease. Pulmonologiya. 2023;33(5):587–594. doi: 10.18093/0869-0189-2023-33-5-587-594 EDN: XWOLJE
  4. Avdeev SN. Pathologic physiology of exacerbations of chronic obstructive pulmonary disease. Messenger of Anesthesiology and Resuscitation. 2019;16(2):75–82. (In Russ.) doi: 10.21292/2078-5658-2019-16-2-75-82 EDN: ZIZXKP
  5. Avdeev SN, Leshchenko IV, Aisanov ZR. Chronic obstructive pulmonary disease (COPD 2024). Clinical guidelines (short version). Journal of Respiratory Medicine. 2025;1(2):5–16. doi: 10.17116/respmed202510215 EDN: HDSQWO
  6. Chuchalin AG, Avdeev SN, Aisanov ZR, et al. Federal guidelines on diagnosis and treatment of chronic obstructive pulmonary disease. Pulmonologiya. 2022;32(3):356–392. doi: 10.18093/0869-0189-2022-32-3-356-392 EDN: ANYVUN
  7. Acampora G, Cook DJ, Rashidi P, Vasilakos AV. A survey on ambient intelligence in health care. Proc IEEE Inst Electr Electron Eng. 2013;101(12):2470–2494. doi: 10.1109/JPROC.2013.2262913
  8. Aminizadeh S, Heidari A, Dehghan M, et al. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artif Intell Med. 2024;149:102779. doi: 10.1016/j.artmed.2024.102779 EDN: UQCECM
  9. Kaplan A, Cao H, FitzGerald JM, et al. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J Allergy Clin Immunol Pract. 2021;9(6):2255–2261. doi: 10.1016/j.jaip.2021.02.014 EDN: OVTMJU
  10. Song W, Han J, Deng S, et al. Joint energy-based model for semi-supervised respiratory sound classification: a method of insensitive to distribution mismatch. IEEE J Biomed Health Inform. 2025;29(2):1433–1443. doi: 10.1109/JBHI.2024.3480999
  11. Kucher AV, Khodus SV, Borzenko ES. Issues and challenges in the use of artificial intelligence in medicine. Bulletin Physiology and Pathology of Respiration. 2024;(94):135–140. doi: 10.36604/1998-5029-2024-94-135-140 EDN: IKKEXV
  12. Haider NS, Singh BK, Periyasamy R, Behera AK. Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. J Med Syst. 2019;43(8):255. doi: 10.1007/s10916-019-1388-0 EDN: XEFMXK
  13. Levy J, Álvarez D, Del Campo F, Behar JA. Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers. Physiol Meas. 2021;42(5). doi: 10.1088/1361-6579/abf5ad EDN: DVIXPN
  14. Yin H, Wang K, Yang R, et al. A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients. Comput Methods Programs Biomed. 2024;246:108005. doi: 10.1016/j.cmpb.2023.108005 EDN: OSXNSI
  15. Wu CT, Wang SM, Su YE, et al. A precision health service for chronic diseases: development and cohort study using wearable device, machine learning, and deep learning. IEEE J Transl Eng Health Med. 2022;10:2700414. doi: 10.1109/JTEHM.2022.3207825
  16. Xie W, Fang Y, Yang G, et al. Transformer-based multi-modal data fusion method for COPD classification and physiological and biochemical indicators identification. Biomolecules. 2023;13(9):1391. doi: 10.3390/biom13091391 EDN: XFEMOX
  17. Merone M, Pedone C, Capasso G, et al. A decision support system for tele-monitoring copd-related worrisome events. IEEE J Biomed Health Inform. 2017;21(2):296–302. doi: 10.1109/JBHI.2017.2654682
  18. Gálvez-Barrón C, Pérez-López C, Villar-Álvarez F, et al. Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease. Sci Rep. 2023;13(1):12709. doi: 10.1038/s41598-023-39329-6 EDN: WDJJGW
  19. Zhang J, Sun K, Jagadeesh A, et al. The potential and pitfalls of using a large language model such as ChatGPT, GPT-4, or LLaMA as a clinical assistant. J Am Med Inform Assoc. 2024;31(9):1884–1891. doi: 10.1093/jamia/ocae184 EDN: YULMEJ
  20. Chamberlain DB, Kodgule R, Fletcher RR. A mobile platform for automated screening of asthma and chronic obstructive pulmonary disease. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:5192–5195. doi: 10.1109/EMBC.2016.7591897
  21. Roy A, Satija U, Karmakar S. Pulmo-TS2ONN: a novel triple scale self operational neural network for pulmonary disorder detection using respiratory sounds. IEEE Trans Instrum Meas. 2024;73:1–12. doi: 10.1109/TIM.2024.3378206
  22. Altan G, Kutlu Y, Allahverdi N. Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE J Biomed Health Inform. 2019. doi: 10.1109/JBHI.2019.2931395 EDN: DHAQEO
  23. Davies HJ, Bachtiger P, Williams I, et al. Wearable in-ear PPG: detailed respiratory variations enable classification of COPD. IEEE Trans Biomed Eng. 2022;69(7):2390–2400. doi: 10.1109/TBME.2022.3145688 EDN: QBLSYE
  24. Kanwade AB, Sardey MP, Panwar SA, et al. Combined weighted feature extraction and deep learning approach for chronic obstructive pulmonary disease classification using electromyography. Int J Inf Technol. 2024;16(3):1485–1494. doi: 10.1007/s41870-023-01498-y EDN: OAIQVA
  25. Wang Q, Wang H, Wang L, Yu F. Diagnosis of chronic obstructive pulmonary disease based on transfer learning. IEEE Access. 2020;8:47370–47383. doi: 10.1109/access.2020.2979218 EDN: FRDXAD
  26. Tran-Anh D, Vu NH, Nguyen-Trong K, Pham C. Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare. Pervasive Mob Comput. 2022;86:101685. doi: 10.1016/j.pmcj.2022.101685 EDN: LAINBS
  27. Li J, Wang C, Chen J, et al. Nandi Explainable CNN with fuzzy tree regularization for respiratory sound analysis. IEEE Trans Fuzzy Syst. 2022;30(6):1516–1528. doi: 10.1109/TFUZZ.2022.3144448
  28. Zhang Y, Xia T, Han J, et al. Towards open respiratory acoustic foundation models: pretraining and benchmarking. NeurIPS. 2024;37:27024–27055. doi: 10.48550/arXiv.2406.16148
  29. Kumar S, Bhagat V, Sahu P, et al. A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases. Comput Methods Programs Biomed. 2024;243:107911. doi: 10.1016/j.cmpb.2023.107911 EDN: YKKESM
  30. Rahman MJ, Nemati E, Rahman M, et al. Toward early severity assessment of obstructive lung disease using multi-modal wearable sensor data fusion during walking. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:5935–5938. doi: 10.1109/EMBC44109.2020.9176559
  31. Abineza C, Balas VE, Nsengiyumva P. A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use. JIFS. 2022;43(2):1683–1695. doi: 10.3233/JIFS-219270 EDN: HJLXPF
  32. Roy A, Satija U. A novel melspectrogram snippet representation learning framework for severity detection of chronic obstructive pulmonary diseases. IEEE Trans Instrum Meas. 2023;72:1–11. doi: 10.36227/techrxiv.21758660.v1
  33. Patel PJ, Diwan D, Patel KA, et al. Multi feature fusion for COPD classification using deep learning algorithms. JIST. 2024;12(4):780. doi: 10.62110/sciencein.jist.2024.v12.780 EDN: NERRYX
  34. Yin C, Udrescu M, Gupta G, et al. Fractional dynamics foster deep learning of copd stage prediction. Adv Sci (Weinh). 2023;10(12):e2203485. doi: 10.1002/advs.202203485 EDN: GBKJLS
  35. Shah SA, Velardo C, Farmer A, Tarassenko L. Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system. J Med Internet Res. 2017;19(3):e69. doi: 10.2196/jmir.7207
  36. Jin Y, Zhang T, Cao Z, et al. Prediction indicators for acute exacerbations of chronic obstructive pulmonary disease by combining non-linear analyses and machine. In: 2018 IEEE International conference on bioinformatics and biomedicine (BIBM); Madrid; 2018. P. 2515–2521. doi: 10.1109/BIBM.2018.8621430
  37. Swaminathan S, Qirko K, Smith T, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS One. 2017;12(11):e0188532. doi: 10.1371/journal.pone.0188532
  38. Nallanthighal VS, Härmä A, Strik H. Detection of COPD exacerbation from speech: comparison of acoustic features and deep learning based speech breathing models. In: ICASSP 2022-2022 IEEE International conference on acoustics, speech and signal processing; Singapore; 2022. P. 9097–9101. doi: 10.1109/ICASSP43922.2022.9747785
  39. Kor CT, Li YR, Lin PR, et al. Explainable machine learning model for predicting first-time acute exacerbation in patients with chronic obstructive pulmonary disease. J Pers Med. 2022;12(2):228. doi: 10.3390/jpm12020228 EDN: MCKOYI
  40. Weng Y, Fang Y, Yan H, et al. Bayesian non-parametric classification with tree-based feature transformation for NIPPV efficacy prediction in COPD patients. IEEE Access. 2019;7:177774–177783. doi: 10.1109/ACCESS.2019.2958047
  41. Chouvarda I, Philip NY, Natsiavas P, et al. WELCOME – innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with comorbidities. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3180–3183. doi: 10.1109/EMBC.2014.6944298
  42. Xie W, Gaydecki P, Caress AL. An inhaler tracking system based on acoustic analysis: Hardware and software. IEEE Trans Instrum Meas. 2019;68(11):4472–4480. doi: 10.1109/TIM.2018.2886978
  43. Ali HAEM, Al-Adl AS. Electrophysiological biomarkers of central nervous system affection in cases of chronic obstructive pulmonary disease (COPD). Egypt J Neurol Psychiat Neurosurg. 2021;57(1):74. doi: 10.1186/s41983-021-00311-6 EDN: ZXAHQN
  44. Bliznuks D, Cizovs J, Freimanis D, et al. Approaching automated COPD treatment based on Markov decision process. In: 2023 IEEE 64th International scientific conference on information technology and management science of Riga technical university (ITMS); Riga; 2023. P. 1–5. doi: 10.1109/ITMS59786.2023.10317773
  45. Shuvo SB, Ali SN, Swapnil SI, et al. A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid scalogram. IEEE J Biomed Health Inform. 2021;25(7):2595–2603. doi: 10.1109/JBHI.2020.3048006 EDN: MTQDTQ
  46. Zafari H, Langlois S, Zulkernine F, et al. Predicting chronic obstructive pulmonary disease from emr data. In: 2020 IEEE Conference on computational intelligence in bioinformatics and computational biology (CIBCB); Via del Mar; 2020. P. 1–8. doi: 10.1109/CIBCB48159.2020.9277712
  47. Kumar S, Shvetsov AV, Alsamhi SH. FuzzyGuard: a novel multimodal neuro-fuzzy framework for COPD early diagnosis. IEEE Internet Things J. 2024;12(8):9627–9637. doi: 10.1109/JIOT.2024.3467176
  48. Nemati E, Xu X, Nathan V, et al. UbiLung: multi-modal passive-based lung health assessment. In: ICASSP 2022-2022 IEEE International conference on acoustics, speech and signal processing; Singapore; 2022. P. 551–555. doi: 10.1109/ICASSP43922.2022.9746614
  49. Epiu I, Gandevia SC, Boswell-Ruys CL, et al. Respiratory-related evoked potentials in chronic obstructive pulmonary disease and healthy aging. Physiol Rep. 2022;10(23):e15519. doi: 10.14814/phy2.15519 EDN: MQXEWB
  50. Dally EC, Rekha BB. Automated chronic obstructive pulmonary disease (COPD) detection and classification using mayfly optimization with deep belief network model. Biomed Signal Process Control. 2024;96(Part A):106488. doi: 10.1016/j.bspc.2024.106488

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