Artificial Intelligence in Management of Respiratory Disease

Yıl 2024, Cilt: 3 Sayı: 2, 69 - 75, 31.08.2024

Erhan Kızmaz

https://doi.org/10.5281/zenodo.13621377

Öz

This study aims to summarize the role of artificial intelligence in the management of respiratory diseases in the light of the literature. Artificial intelligence is a developing technology that achieves fast and effective results using machine learning, deep learning and data analysis methods. It is useful to use in situations that require high dedication and complex and fast decision-making mechanisms. Respiratory diseases, which are increasing worldwide and have a high risk of mortality and morbidity, pose a great burden to both the healthcare system and healthcare professionals. The literature supports the use of artificial intelligence in the management of respiratory diseases in order to reduce this burden, make critical decisions, and benefit patients and healthcare professionals. Complicated imaging methods, monitoring symptoms, and predicting possible situations and side effects are the aspects where artificial intelligence will influence this field.

Anahtar Kelimeler

Artificial intelligence, Respiratory disease, Machine learning, Deep learning

Solunum Sistemi Hastalıklarıyla Mücadelede Yapay Zeka

Öz

Bu çalışma, solunum yolu hastalıklarının yönetiminde yapay zekanın rolünü literatür ışığında özetlemeyi amaçlamaktadır. Yapay zeka, makine öğrenmesi, derin öğrenme ve veri analizi yöntemlerini kullanarak hızlı ve etkili sonuçlar elde eden, gelişen bir teknolojidir. Yüksek özveri gerektiren, karmaşık ve hızlı karar alma mekanizmaları gerektiren durumlarda kullanılması faydalıdır. Dünya çapında giderek artan, mortalite ve morbidite riski yüksek olan solunum yolu hastalıkları hem sağlık sistemi hem de sağlık çalışanları için büyük bir yük oluşturmaktadır. Literatür, solunum yolu hastalıklarının yönetiminde bu yükün azaltılması, kritik kararların alınması, hastalara ve sağlık profesyonellerine fayda sağlanması amacıyla yapay zekanın kullanımını desteklemektedir. Karmaşık görüntüleme yöntemleri, semptomların izlenmesi, olası durumların ve yan etkilerin tahmin edilmesi yapay zekanın bu alanı etkileyeceği yönlerdir.

Anahtar Kelimeler

Yapay zeka, Solunum sistemi hastalıkları, Makine öğrenme, Derin öğrenme

Kaynakça

• Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 2020;75(8):695-701.
• Zhang C, Wu W, Yang J, Sun J. Application of artificial intelligence in respiratory medicine. JDH. 2022;1(1):30-9.
• Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics. 2023;13(3):357. https://doi.org/10.3390/diagnostics13030357.
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44.
• Buch V. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):143-4.
• Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020;14(6):559-64.
• Chawla J, Walia NK. Artificial Intelligence based Techniques in Respiratory Healthcare Services: A Review. 2022 3rd International Conference on Computing, Analytics and Networks (ICAN). 2022;1-4. doi: 10.1109/ICAN56228.2022.10007236.
• Catherwood P, Rafferty J, McLaughlin J. Artificial Intelligence for Long-term Respiratory Disease Management. Paper presented at: British HCI Conference 2018; Belfast, Northern Ireland.
• Mo Z, et al. Acute effects of air pollution on respiratory disease mortalities and outpatients in Southeastern China. Nature. 2018;8:1-9.
• British Lung Foundation. Lung disease in the UK. Available from: https://statistics.blf.org.uk/lung-disease-uk-big-picture. Accessed 2018 Jun 5.
• Komarow HD, et al. Impulse oscillometry in the evaluation of diseases of the airways in children. Ann Allergy Asthma Immunol. 2011;106:191-9.
• Siu BMK, Kwak GH, Ling L, Hui P. Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches. Sci Rep. 2020;10:20931.
• Artigas L, Coma M, Matos-Filipe P, Aguirre-Plans J, Farrés J, Valls R, et al. In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm. PLoS One. 2020;15
• Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JW, 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):2255-61.
• Global Initiative for Asthma. GINA Report, Global Strategy for Asthma Management and Prevention. 2020. Available from: https://ginasthma.org/ginareports/. Accessed 2020 Apr 13.
• Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for Prevention, Diagnosis and Management of COPD. 2020. Available from: https://goldcopd.org/gold-reports/. Accessed 2020 Apr 13.
• Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020. doi: 10.1080/17476348.2020.1743181.
• Pramono RXA, Imtiaz SA, Rodriguez-Villegas E. A cough-based algorithm for automatic diagnosis of pertussis. PLoS One. 2016;11
• Gabaldón-Figueira JC, Keen E, Giménez G, et al. Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence. ERJ Open Res. 2022;8:00053-2022. doi: 10.1183/23120541.00053-2022.
• Paredes M. Can artificial intelligence help reduce human medical error? Two examples from ICUs in the US and Peru. 2018.
• European Lung Foundation. U-BIOPRED Project. Available from: https://www.europeanlung.org/en/projects-and-research/projects/u-biopred/home.
• Raja MA, Loughran R, Mc Caffery F. A review of applications of artificial intelligence in cardiorespiratory rehabilitation. Inform Med Unlocked. 2023;101327.
• Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for Prevention, Diagnosis and Management of COPD. 2023;5-146.
• Lamberton CE, Mosher C L. Review of the Evidence for Pulmonary Rehabilitation in COPD: Clinical Benefits and Cost-Effectiveness. Respiratory Care. 2024;69(6): 686-696.
• del Valle MF, Valenzuela J, Bascour-Sandoval C, Nasri Marzuca G, del Sol M, Canales C et al. Effects of a pulmonary rehabilitation program on pulmonary function, exercise performance, and quality of life in patients with severe COVID-19. Ther Adv Respir Dis. 2024;18:17534666231212431. doi:10.1177/17534666231212431
• Armenta-Garcia A, Gonzalez-Navarro FF, Caro-Gutierrez J, Flores-Rios BL, Ibarra-Esquer JE. Breml: A breathing rate estimator using wi-fi channel state information and machine learning. 2021 Mexican International Conference on Computer Science (ENC). 2021;1-8.
• Venkat S, PS MTPA, Alex A, Preejith SP, Christopher DJ, Joseph J, et al. Machine learning based spo2 computation using reflectance pulse oximetry. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019;482-5.