Breathing conditions pertaining to nasal obstruction, obstructive sleep apnea, and airflow resistance in the human lower airways have been investigated extensively by researchers over the years. Due to the availability of advanced computer numerical models, such as computational fluid dynamics (CFD), researchers have made progressive studies of airflow characteristic, especially the effects of airflow pressure, velocity and wall shear stress in human obstructive airways. Studies utilizing CFD have enhanced clinical understanding of the physiology and pathophysiology of the respiratory system through the concept of three-dimensional models that facilitate airflow simulation. The main objective of this article is to review recent CFD literature on nasal airflow and lower airway obstruction. The review covers the role of segmentation threshold in the outcome of airflow simulation in the nasal cavity, and results of fluid structure interaction (FSI) and computational fluid dynamics in nasal obstruction and airway collapse in obstructive sleep apnea were also correlated. For models of the lower airways, we evaluated the effect of extra-thoracic airway (ETA) on downstream airflow during simulation against the popular Weibel’s model. In the concluding section, we discussed the advantages, limitations, and prospects (precisely with deep machine learning) of computational fluid dynamics in the clinical assessment and investigation of respiratory diseases.
| Published in | International Journal of Biomedical Science and Engineering (Volume 9, Issue 2) |
| DOI | 10.11648/j.ijbse.20210902.12 |
| Page(s) | 16-26 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Computational Fluid Dynamics (CFD), Fluid-structure Interaction (FSI), Airway Obstruction, Segmentation Threshold (ST), Obstructive Sleep Apnea (OSA)
| [1] | Witherden and Jameson, (2017) Future Directions of Computational Fluid Dynamics American Institute of Aeronautics and Astronautics. http://aerocomlab.stanford.edu/Papers/aiaa_cfd_future_2017.pdf. |
| [2] | Jeny Emmanuel (2020). Application of Computational Fluid Dynamics To Assess The Aerodynamics Of Upper Airway In The Field Of Orthodontics – A Review. European Journal of Molecular & Clinical Medicine. ISSN 2515-8260 Volume 07, Issue 03, 2020. https://ejmcm.com/article_5972_48926d2a6f09c16f0f95ef398063eaad.pdf. |
| [3] | Wong, Chong Y., Solnordal, Christopher B., and Jie Wu. "CFD Modeling and Experimental Observations of Changing Surface Profiles Caused by Solid-Particle Erosion." SPE Prod & Oper 29 (2014): 61–74. doi: https://doi.org/10.2118/157983-PA. |
| [4] | Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM. Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol. 2002; 9 (10): 1153–68. [PubMed] [Google Scholar]. |
| [5] | Muhsun, S. S., Saleh, M. S. & Qassim, A. R. Physical and CFD Simulated Models to Analyze the Contaminant Transport through Porous Media under Hydraulic Structures. KSCE J CivEng 24, 3674–3691 (2020). https://doi.org/10.1007/s12205-020-1767-6. |
| [6] | Chen, Qikun & Peng, Shanbi & Liu, Enbin. (2020). The Role of Computational Fluid Dynamics Tools on Investigation of pathogen transmission: Prevention and Control. Science of The Total Environment. 746. 142090. 10.1016/j.scitotenv.2020.142090. |
| [7] | Khandve, Pravin & Shelke, R. (2012). Application of CFD in Environment Engineering. 6. https://www.researchgate.net/publication/292695531_Application_of_CFD_in_Environment_Engineering. |
| [8] | Ana Fernández Tena, Pere Casan Clarà (2015) Use of Computational Fluid Dynamics in Respiratory Medicine. Vol. 51. Issue 6. pages 293-298. DOI: 10.1016/j.arbr.2015.03.00. |
| [9] | Woldeamanuel, G. G., Mingude, A. B. & Geta, T. G. Prevalence of chronic obstructive pulmonary disease (COPD) and its associated factors among adults in Abeshge District, Ethiopia: a cross sectional study. BMC Pulm Med 19, 181 (2019). https://doi.org/10.1186/s12890-019-0946-z D'Urzo, A. D., Tamari, I., Bouchard, J., Jhirad, R., &Jugovic, P. (2011). Limitations of a spirometry interpretation algorithm. Canadian family physician Medecin de famille canadien, 57 (10), 1153–1156. |
| [10] | World Health Organization, World Health Statistics 2008, http://www.who.int/whosis/whostat/2008/en/,2008. |
| [11] | Gamboa F, Fernandez G, Padilla E, Manterola JM, Lonca J, Cardona PJ, Matas L, Ausina V. Comparative evaluation of initial and new versions of the Gen-Probe Amplified Mycobacterium Tuberculosis Direct Test for direct detection of Mycobacterium tuberculosis in respiratory and nonrespiratory specimens. J Clin Microbiol. 1998 Mar; 36 (3): 684-9. doi: 10.1128/JCM.36.3.684-689.1998. PMID: 9508296; PMCID: PMC104609. |
| [12] | Heder de Vries, Annemijn Jonkman, Zhong-Hua Shi, Angélique Spoelstra-de Man, Leo Heunks Ann Transl Med. 2018 Oct; 6 (19): 387. doi: 10.21037/atm.2018.05.53. PMCID: PMC621236. |
| [13] | K. P. Van de Woestijne, H. Franken, M. Cauberghs, F. J. Làndsér, J. Clément (1981) A modification of the forced oscillation technique. Pages 655-660, ISBN 9780080268231, https://doi.org/10.1016/B978-0-08-026823-1.50083-1. |
| [14] | J. V. Rundo (2014) Polysomnography; Technique. Encyclopedia of the Neurological Sciences (Second Edition). Academic Press, Pages 930-935, ISBN 9780123851581, https://doi.org/10.1016/B978-0-12-385157-4.00537-6. |
| [15] | Joseph Feher (2017) Lung Volumes and Airway Resistance. Quantitative Human Physiology (Second Edition), Academic Press, 2017, Pages 633-641, ISBN 9780128008836, https://doi.org/10.1016/B978-0-12-800883-6.00061-6. |
| [16] | Joe L. Mauderly (1990) Measurement of Respiration and Respiratory Responses During Inhalation Exposures. Journal of the American college of toxicology Volume 9, Number 4, 1990 Mary Ann Liebert, Inc., Publishers. |
| [17] | J A. Richards (Consultant Pulmonologist) (2006) Office spirometry—indications and limitations, South African Family Practice, 48: 2, 48-51, DOI: 10.1080/20786204.2006.10873340.: https://doi.org/10.1080/20786204.2006.10873340. |
| [18] | H. Normand, F. Normand, X. Le Coutour, M-A. Metges, A. Mouadil (2007). Clinical evaluation of a screen pneumotachograph as an in-line filter, European Respiratory Journal Aug 2007, 30 (2) 358-363; DOI: 10.1183/09031936.06.00148105. |
| [19] | E. Oostveen. D. MacLeod, H. Lorino, R. Farré, Z. Hantos, K. Desager, and F. Marchal (2013). The forced oscillation technique in clinical practice: methodology, recommendations and future developments; European Respiratory Society. DOI: https://doi.org/10.1183/09031936.03.00089403. PubMed: 14680096. |
| [20] | Dorottya Czovek, Claire Shackleton, Kate Taylor, Zoltan Gingl, Zoltan Hantos, Peter Sly (2015) Limitations of forced spirometry in the detection of bronchodilator response in asthmatic children; European Respiratory Journal. Sep 2015, 46 (suppl 59) PA1258; DOI: 10.1183/13993003.congress-2015.PA1258. |
| [21] | Riazuddin, V. N.; Zubair, M.; Abdullah, M. Z.; Ismail, R.; Shuaib, I. L.; Hamid, S. A.; Ahmad, K. A. Numerical study of inspiratory and expiratory flow in a human nasal cavity. J. Med Biol. Eng. 2011, 31, 201–206. |
| [22] | Yijuan Di, MinruiFei, Xin Sun, and T. C. Yang (2010) Modeling of the Human Bronchial Tree and Simulation of Internal Airflow. Springer-Verlag Berlin Heidelberg; K. Li et al. (Eds.): LSMS/ICSEE 2010, Part I, LNCS 6328, pp. 456–465, 2010. |
| [23] | Fernández Tena A, Casan Clarà P. Use of computational fluid dynamics in respiratory medicine. Arch Bronconeumol. 2015 Jun; 51 (6): 293-8. English, Spanish. doi: 10.1016/j.arbres.2014.09.005. Epub 2015 Jan 22. PMID: 25618456. |
| [24] | SCONA (2018) Society for Computational Fluid Dynamics of the Nose and Airway. Confrence Program London, UK. www.SCONA_2018_Detailed_Program_FINAL4.pdf. |
| [25] | Heenan, A.; Matida, E.; Pollard, A.; Finlay, W. Experimental measurements and computational modeling of the flow field in an idealized human oropharynx. Exp. Fluids 2003, 35, 70–84. |
| [26] | Weinhold I, Mlynski G. Numerical simulation of airflow in the human nose. Eur Arch Otorhinolaryngol 2004; 261: 452-455. |
| [27] | Zhao, K., Malhotra, P., Rosen, D., Dalton, P., &Pribitkin, E. A. (2014). Computational fluid dynamics as surgical planning tool: a pilot study on middle turbinate resection. Anatomical record (Hoboken, N. J.: 2007), 297 (11), 2187–2195. https://doi.org/10.1002/ar.23033. |
| [28] | Faizal WM, Ghazali NNN, Khor CY, et al. Computational fluid dynamics modelling of human upper airway: A review. Comput. Methods Programs Biomed. 2020; 196: 105627. doi: 10.1016/j.cmpb.2020.105627. |
| [29] | Ezzie, M. E., Parsons, J. P., Mastronarde, J. G.: Sleep and obstructive l ung diseases. Sleep Med. Clin. 3 (4), 505–515 (2008). |
| [30] | Endalew Getnet T sega (2018) Computational Fluid Dynamics Modeling of Respiratory Airflow in Tracheobronchial Airways of Infant, Child, and Adult; Hindawi Computational and Mathematical Methods in Medicine Volume 2018, Article ID 9603451, 9 pages https://doi.org/10.1155/2018/9603451. |
| [31] | Lanlan Zhang, Lixiu He, Jin Gong, Chuntao Liu, "Risk Factors Associated with Irreversible Airway Obstruction in Asthma: A Systematic Review and Meta-Analysis", BioMed Research International, vol. 2016, Article ID 9868704, 10 pages, 2016. https://doi.org/10.1155/2016/9868704. |
| [32] | S. Kumar and R. Salib (2006), Upper airway obstruction is a serious and potentially life-threatening condition and as such requires prompt assessment and management. In Encyclopedia of Respiratory Medicine. |
| [33] | Casey, K. P., Borojeni, A. A. T., Koenig, L. J., Rhee, J. S., and Garcia, G. J. M. (2017). Correlation between Subjective Nasal Patency and Intranasal Airflow Distribution. Correlation between subjective nasal patency and intranasal airflow distribution. Otolaryngol Head Neck Surg. 156: 741-750. |
| [34] | Gaberino C., Rhee, J. S., Garcia, G. J. (2018). Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency. Respir Physiol Neurobiol. 238: 23-32. |
| [35] | Cherobin GB, Voegels RL, Gebrim EMMS, Garcia GJM (2018) Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold. PLoS ONE 13 (11): e0207178. https://doi.org/10.1371/journal. pone.0207178. |
| [36] | Leong, S & Chen, X & Lee, Hp & Wang, DY. (2010). A review of the implications of computational fluid dynamic studies on nasal airflow and physiology. Rhinology. 48. 139-45. 10.4193/Rhin09.133. |
| [37] | Last, Carsten & Winkelbach, Simon & Wahl, Friedrich & Eichhorn, Klaus & Bootz, Friedrich. (2010). A Model-Based Approach to the Segmentation of Nasal Cavity and Paranasal Sinus Boundaries. 6376. 333-342. 10.1007/978-3-642-15986-2_34. |
| [38] | Bui NL, Ong SH, Foong KW. Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images. Int J Comput Assist Radiol Surg. 2015 Aug; 10 (8): 1269-77. doi: 10.1007/s11548-014-1134-5. Epub 2014 Dec 12. PMID: 25503593. |
| [39] | Girardin, M., Bilgen, E., Arbour, P.: Experimental study of velocity fields in a human nasal fossa by laser anemometry. Ann. Otol. Rhinol. Laryngol. 92 (3 pt 1), 231–236 (1983). |
| [40] | Zhao, Y., Lieber, B. B.: Steady inspiratory flow in a model symmetric bifurcation. J. Biomech. Eng. 116 (4), 488–496 (1994). |
| [41] | Corcoran, T. E., Chigier, N.: Characterization of the laryngeal jet using phase doppler interferometry. J. Aerosol. Med. 13 (2), 125–137 (2002). |
| [42] | Kim, S. K., Chung, S. K.: An investigation on airflow in disordered nasal cavity and its corrected models by tomographic PIV. Meas. Sci. Technol. 15 (6), 1090–1096 (2004). |
| [43] | Ying Wang, Yingxi Liu, Xiuzhen Sun, Shen Yu, Fei Gao (2009) Numerical analysis of respiratory flow patterns within human upper airway. The Chinese Society of Theoretical and Applied Mechanics and Springer-Verlag GmbH 2009; Acta Mech Sin (2009) 25: 737–746 DOI 10.1007/s10409-009-0283-1. |
| [44] | Stapleton, K. W., Guentsch, E., Hoskinson, M. K., et al.: On the suitability of κ–ε turbulence modeling for aerosol deposition in the mouth and throat: a comparison with experiment. J. Aerosol. Sci. 31 (6), 739–749 (2012). |
| [45] | Jin, H. H., Fan, J. R., Zeng, M. J., et al.: Large eddy simulation of inhaled particle deposition within the human upper respiratory tract. J. Aerosol Sci. 38 (3), 257–268 (2007). |
| [46] | Subramaniam, R. P., Richardson, R. B., Morgan, K. T., et al.: Computational fluid dynamics simulations of inspiratory airflow in the human nose and nasopharynx. Inhal. Toxicol. 10 (5), 473– 502 (1998). |
| [47] | Ma, B., Lutchen, K.: CFD simulation of aerosol deposition in an anatomically based human large-medium airway model. Ann. Biomed. Eng. 37 (2), 271–285 (2009). |
| [48] | Yu, S., Liu, Y. X., Sun, X. Z., et al.: Influence of nasal structure on the distribution of airflow in nasal cavity. Rhinology 46 (2), 137– 143 (2008). |
| [49] | Renotte, C., Bouffioux, V., Wilquem, F.: Numerical 3D analysis of oscillatory flow in the time-varying laryngeal channel. J. Biomech. 33 (12), 1637–1644 (2002). |
| [50] | Doorly, D. J., Taylor, D. J., Schroter, R. C.: Mechanics of airflow in the human nasal airways. Respir. Physiol. Neurobiol. 163 (1–3), 100–110 (2008). |
| [51] | Calay, R. K., Kurujareon, J., Holdø, A. E.: Numerical simulation of respiratory flow patterns within human lung. Respir. Physiol. Neurobiol. 130 (2), 201–221 (2002). |
| [52] | Lin, C. L., Tawhai, M. H., McLennan, G., et al.: Characteristics of the turbulent laryngeal jet and its effect on airflow in the human intra-thoracic airways. Respir. Physiol. Neurobiol. 157 (2–3), 295–309 (2007). |
| [53] | Zhang, Z., Kleinstreuer, C.: Transient airflow structures and particle transport in a sequentially branching lung airway model. Phys. Fluids 14 (2), 862–880 (2002). |
| [54] | Xu, C., Sin, S. H., McDonough, J. M., et al.: Computational fluid dynamics modeling of the upper airway of children with obstructive sleep apnea syndrome in steady flow. J. Biomech. 39 (11), 2043–2054 (2006). |
| [55] | Sun, X. Z., Yu, C., Wang, Y., et al.: Numerical simulation of soft palate movement and airflow in human upper airway by fluid-structure interaction method. Acta. Mech. Sin. 23 (4), 359–367 (2007). |
| [56] | Freitas RK, Schroder W: Numerical investigation of the three-dimensional flow in a human lung model. J Biomech 2008, 41 (11): 2446–2457 |
| [57] | Banno, K., Kryger, M. H.: Sleep apnea: Clinical investigations in humans. Sleep Med. 8 (4), 400–426 (2007). |
| [58] | Jeong, S. J., Kim, W. S., Sung, S. J.: Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics. Med. Eng. Phys. 29 (6), 637–651 (2007). |
| [59] | Chouly, F., A. Van Hirtum, P. Y. Lagree, X. Pelorson, and Y. Payan. 2008. Numerical and experimental study of expiratory flow in the case of major upper airway obstructions with fluid-structure interaction. J. Fluids Struct. 24: 250–269. |
| [60] | Pirnar, J., L. Dolenc-Groselj, I. Fajdiga, and I. Zun. 2015. Computational fluid-structure interaction simulation of airflow in the human upper airway. J. Biomech. 48: 3685–3691. |
| [61] | Subramaniam, D. R., G. Mylavarapu, R. J. Fleck, R. S. Amin, S. R. Shott, and E. J. Gutmark. 2017. Effect of airflow and material models on tissue displacement for surgical planning of pharyngeal airways in pediatric down syndrome patients. J. Mech. Behav. Biomed. Mater. 71: 122–135. |
| [62] | Trung B. Le, Masoud G. Moghaddam, B. Tucker Woodson & Guilherme J. M. Garcia (2019). Airflow limitation in a collapsible model of the human pharynx: physical mechanisms studied with fluid-structure interaction simulations and experiments. Physiol Rep, 7 (10), 2019, e14099, https://doi.org/10.14814/phy2.14099. |
| [63] | Chang, K. K., K. B. Kim, M. W. McQuilling, and R. Movahed. 2018. Fluid structure interaction simulations of the upper airway in obstructive sleep apnea patients before and after maxillomandibular advancement surgery. Am. J. Orthod. Dentofac. 153: 895–904. |
| [64] | Liu, Y., J. Mitchell, Y. Chen, W. Yim, W. Chu, and R. C. Wang. 2018. Study of the upper airway of obstructive sleep apnea patient using fluid structure interaction. Respir. Physiol. Neurobiol. 249: 54–61. |
| [65] | Chouly Zhao, M., T. Barber, P. A. Cistulli, K. Sutherland, and G. Rosengarten. 2013. Simulation of upper airway occlusion without and with mandibular advancement in obstructive sleep apnea using fluid-structure interaction. J. Biomech. 46: 2586–2592. |
| [66] | Dawson, S. V., and E. A. Elliott. 1977. Wave-speed limitation on expiratory flow-a unifying concept. J. Appl. Physiol. Respir. Environ. Exerc. Physiol. 43: 498–515. |
| [67] | Wellman, A., P. R. Genta, R. L. Owens, B. A. Edwards, S. A. Sands, S. H. Loring, et al. 2014. Test of the Starling resistor model in the human upper airway during sleep. J. Appl. Physiol. 117: 1478–1485. |
| [68] | Genta, P. R., S. A. Sands, J. P. Butler, S. H. Loring, E. S. Katz, B. G. Demko, et al. 2017. Airflow shape is associated with the pharyngeal structure causing OSA. Chest 152: 537–546. |
| [69] | Chaoqun Fang, Xiu Ying Wang, David Dagan Feng, Airway Segmentation for Low-Contrast CT Images from Combined PET/CT Scanners Based on Airway Modeling and Seed Prediction, IFAC Proceedings Volumes, Volume 42, Issue 12, 2009, Pages 198-203, ISSN 1474-6670, ISBN 9783902661494, https://doi.org/10.3182/20090812-3-DK-2006.0051. |
| [70] | Artaechevarria, Xabier & Pérez-Martín, D & Ceresa, Mario & de Biurrun, Gabriel & Blanco, D & Montuenga, L & Ginneken, B & Ortiz-de-Solorzano, Carlos & Muñoz-Barrutia, Arrate. (2009). Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT. Physics in medicine and biology. 54. 7009-24. 10.1088/0031-9155/54/22/017. |
| [71] | Gil D, Sanchez C, Borras A, Diez-Ferrer M, Rosell A (2019) Segmentation of distal airways using structural analysis. PLOS ONE 14 (12): e0226006. https://doi.org/10.1371/journal.pone.0226006. |
| [72] | Reynisson PJ, Scali M, Smistad E, Hofstad EF, Leira HO, Lindseth F, et al. (2015) Airway Segmentation and Centerline Extraction from Thoracic CT – Comparison of a New Method to State of the Art Commercialized Methods. PLoS ONE 10 (12): e0144282. https://doi.org/10.1371/journal.pone.0144282. |
| [73] | Nakano H, Mishima K, Ueda Y, Matsushita A, Suga H, Miyawaki Y, et al. A new method for determining the optimal CT threshold for extracting the upper airway. Dentomaxillofacial Radiol. 2013; 42 (3): 26397438 10.1259/dmfr/26397438. [PMC free article] [PubMed] [Google Scholar]. |
| [74] | Quadrio, M., Pipolo, C., Corti, S. et al. Effects of CT resolution and radiodensity threshold on the CFD evaluation of nasal airflow. Med Biol Eng Comput 54, 411–419 (2016). https://doi.org/10.1007/s11517-015-1325-4. |
| [75] | Kawarai Y, Fukushima K, Ogawa T, Nishizaki K, Gunduz M, Fujimoto M, et al. Volume quantification of healthy paranasal cavity by three-dimensional CT imaging. Acta Otolaryngol Suppl. 1999; 540: 45–9. PMID: 10445079. |
| [76] | Weissheimer A, Menezes LM, Sameshima GT, Enciso R, Pham J, Grauer D. Imaging software accuracy for 3-dimensional analysis of the upper airway. Am J Orthod Dentofacial Orthop. 2012; 142 (6): 801–13. https://doi.org/10.1016/j.ajodo.2012.07.015 PMID: 23195366. |
| [77] | Alsufyani NA, Flores-Mir C, Major PW. Three-dimensional segmentation of the upper airway using cone beam CT: a systematic review. Dentomaxill of acRadiol. 2012; 41 (4): 276–84. https://doi.org/10.1259/dmfr/79433138 PMID: 22517995. |
| [78] | David Zwicker et al (2018). Validated reconstructions of geometries of nasal cavities from CT scans. Biomed. Phys. Eng. Express 4 045022. https://doi.org/10.1088/2057-1976/aac6af. |
| [79] | Christer Janson, Guy Marks, Sonia Buist, Louisa Gnatiuc, Thorarinn Gislason, Mary Ann McBurnie, Rune Nielsen, Michael Studnicka, Brett Toelle, Bryndis Benediktsdottir, Peter Burney (2013). The impact of COPD on health status: findings from the BOLD study. European Respiratory Journal 2013 42: 1472-1483; DOI: 10.1183/09031936.00153712. |
| [80] | Armin Ernst, David Feller-Kopman, Heinrich D. Becker, and Atul C. Mehta (2004) Central Airway Obstruction, American Journal of Respiratory and Critical Care Medicine. Volume 169, Issue 12. https://doi.org/10.1164/rccm.200210-1181SO. |
| [81] | Hui Chen, Jie Zhang, Xiaojian Qiu, Juan Wang, Yinghua Pei, Yuling Wang, Ting Wang. (2020) Choice of bronchoscopic intervention working channel for benign central airway stenosis. Internal and Emergency Medicine 40. Online publication date: 23-Oct-2020. Crossref. |
| [82] | Bora Sul, Anders Wallqvist, Michael J. Morris, Jaques Reifman, Vineet Rakesh, (2014). A computational study of the respiratory airflow characteristics in normal and obstructed human airways. Computers in Biology and Medicine, Volume 52, Pages 130-143, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2014.06.008 |
| [83] | D. B. Coultas, D. Mapel, R. Gagnon, E. Lydick, The health impact of undiagnosed airflow obstruction in a national sample of United States adults, Am. J. Respir. Crit. Care Med. 164 (2001) 372–377. |
| [84] | Kolanjiyil, A. V. and Kleinstreuer, C. (2017). Computational analysis of aerosol dynamics in a human whole-lung airway model. J. Aerosol Sci. 114: 301–316. |
| [85] | E. R. Weibel. Morphometry of the human lung: the state of the art after two decades Bull. Eur. Physiopathol. Respir., 15 (1979), pp. 999-1013 View Record in ScopusGoogle Scholar. |
| [86] | K. Horsfield, G. Dart, D. E. Olson, G. F. Filley, G. CummingModels of the human bronchial tree J. Appl. Physiol., 31 (1971), pp. 207-217 CrossRefView Record in ScopusGoogle Scholar. |
| [87] | Z. Zhang, C. Kleinstreuer, C. S. Kim Airflow and nanoparticle deposition in a 16-generation tracheobronchial airway model. Ann. Biomed. Eng., 36 (2008), pp. 2095-2110 CrossRefView Record in ScopusGoogle Scholar. |
| [88] | N. Nowak, P. P. Kakade, A. V. Annapragada Computational fluid dynamics simulation of airflow and aerosol deposition in human lungs. Ann. Biomed. Eng., 31 (2003), pp. 374-390 View Record in ScopusGoogle Scholar. |
| [89] | Brouns M, Jayaraju ST, Lacor C, De Mey J, Noppen M, Vincken W, Verbanck S: Tracheal stenosis: a flow dynamics study. J Appl Physiol 2007, 102 (3): 1178–1184. |
| [90] | De Rochefort L, Vial L, Fodil R, Maître X, Louis B, Isabey D, Caillibotte G, Thiriet M, Bittoun J, Durand E, Sbirlea-Apiou G: In vitro validation of computational fluid dynamic simulation in human proximal airways with hyperpolarized3He magnetic resonance phase-contrastvelocimetry. J Appl Physiol 2012, 102 (5): 2012–2023. |
| [91] | Justus KavitaMutuku, Wei-Hsin Chen (2018) Flow Characterization in Healthy Airways and Airways with Chronic Obstructive Pulmonary Disease (COPD) during Different Inhalation Conditions. Aerosol and Air Quality Research, 18: 2680–2694, 2018 ISSN: 1680-8584 print/ 2071-1409 doi: 10.4209/aaqr.2018.06.0232. |
| [92] | Luo HY, Liu Y: Modeling the bifurcating flow in a CT-scanned human lung airway. J Biomech 2008, 41 (12): 2681–2688. |
| [93] | Gemci T, Ponyavin V, Chen Y, Chen H, Collins R: Computational model of airflow in upper 17 generations of human respiratory tract. J Biomech 2008, 41 (9): 2047–2054. |
| [94] | Lin C, Tawhai MH, McLennan G, Hoffman EA: Multiscale simulation of gas flow in subject specific models ofthe human lung. IEEE Eng Med Biol Mag 2009, 28 (3): 25–33. |
| [95] | Tawhai MH, Hoffman EA, Lin CL: The lung physiome: merging imaging-based measures with predictive computational models. Wiley Interdiscip Rev SystBiol Med 2009, 1 (1): 61–72. |
| [96] | Chen, X., Zhong, W., Sun, B., Jin, B. and Zhou, X. (2012). Study on gas/solid flow in an obstructed pulmonary airway with transient flow based on CFD–DPM approach. Powder Technol. 217: 252–260. |
| [97] | Johnstone A, Uddin M, Pollard A, Heenan A, Finlay W. H., "The flow inside an idealised form of the human extra-thoracic airway," Experiments in Fluids, pp. 37 (5), 673-689, 2004. |
| [98] | Ball, C. G., Uddin, M., & Pollard, A., "High resolution turbulence modelling of airflow in an idealised human extra-thoracic airway," Computers & Fluids, pp. 37 (8), 943-964, 2008a. |
| [99] | Kleinstreuer, C., & Zhang, Z., "Airflow and particle transport in the human respiratory system," Annual Review of Fluid Mechanics, pp. 42, 301-334, 2010. |
| [100] | Jayaraju, S. T., Brouns, M., Lacor, C., Belkassem, B., &Verbanck, S., "Large eddy and detached eddy simulations of fluid flow and particle deposition in a human mouth throat," Journal of Aerosol Science, pp. 39 (10), 862-875, 2008. |
| [101] | Mylavarapu, G., Murugappan, S., Mihaescu, M., Kalra, M., Khosla, S., & Gutmark, E., "Validation of computational fluid dynamics methodology used for human upper airway flow simulations," Journal of biomechanics, pp. 42 (10), 1553-1559, 2009. |
| [102] | Mihaescu, M., Khosla, S. M., Murugappan, S., &Gutmark, E. J., "Unsteady laryngeal airflow simulations of the intra-glottal vortical structures," The Journal of the Acoustical Society of America, pp. 127 (1), 435-444, 2010. |
| [103] | Jamasp Azarnoosh, Kidambi Sreenivas and Abdollah Arabshahi (2016) Computational fluid dynamics simulation of the airflow through the human respiratory tract. The International Conference on Computational Science. Elsevier volume 80, 2016, Pages 965-976. Doi: 10.1016/j.procs.2016.05.392. |
| [104] | Lee, J. H., Na, Y., Kim, S. K., & Chung, S. K., "Unsteady flow characteristics through a human nasal airway," Respiratory physiology & neurobiology, pp. 172 (3), 136-146, 2010. |
| [105] | Yasuo M, Kitaguchi Y, Tokoro Y, Kosaka M, Wada Y, Kinjo T, Ushiki A, Yamamoto H, Hanaoka M. Differences Between Central Airway Obstruction and Chronic Obstructive Pulmonary Disease Detected with the Forced Oscillation Technique. Int J Chron Obstruct Pulmon Dis. 2020; 15: 1425-1434https://doi.org/10.2147/COPD.S246126. |
| [106] | Zhijian Liu, Angui Li, Xiaoxia Xu & Ran Gao (2012) Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper Respiratory Tracts, Engineering Applications of Computational Fluid Mechanics, 6: 4, 556-571, DOI: 10.1080/19942060.2012.11015442 To link to this article: https://doi.org/10.1080/19942060.2012.11015442. |
| [107] | Lee BK. Computational fluid dynamics in cardiovascular disease. Korean Circ J 2011; 41 (8): 423–30. |
| [108] | Iwasaki T, Sato H, Suga H, Minami A, Yamamoto Y, Takemoto Y, et al. Herbst appliance effects on pharyngeal airway ventilation evaluated using computational fluid dynamics. Angle Orthod. 2017; 87 (3): 397–403. |
| [109] | Bockholt U, Mlynski G, Müller W, Voss G. Rhinosurgical therapy planning via endonasal airflow simulation. Comput Aided Surg 2000; 5: 175-179. |
| [110] | Chen, W. H., Lee, K. H., Mutuku, J. K. and Hwang, C. J. (2018). Flow dynamics and PM2.5 deposition in healthy and asthmatic airways at different inhalation statuses. Aerosol Air Qual. Res. 18: 866–883. |
| [111] | Patel, M. (Jan. 2013). “Computational Fluid Dynamics (CFD) Simulation BeneÕts Practical Applications.” Retrieved from http://www.hitechcfd.com/cfd-knowledgebase/cfdsimulationbeneÕts-practical-applications.html. |
| [112] | R. Orihara, R. Narasaki, Y. Yoshinaga, Y. Morioka and Y. Kokojima, "Approximation of Time-Consuming Simulation Based on Generative Adversarial Network," 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 2018, pp. 171-176, doi: 10.1109/COMPSAC.2018.10223. |
| [113] | Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020 Jun; 14 (6): 559-564. doi: 10.1080/17476348.2020.1743181. Epub 2020 Mar 17. PMID: 32166988. |
| [114] | CR. Btech (2017). “Computational Fluid Dynamics And Applications.” Retrieved from http://crbtech.in/CAD-CAMTraining/computational-Öuid-dynamics-applications/ |
| [115] | Red Metal Mining (2021) What Is Computational Fluid Dynamics (CFD)? Application & Advantages: https://redmetal.co.za/engineering-services/computational-fluid-dynamics flow-simulation/. |
| [116] | Sheikhtaheri A, Sadoughi F and Hashemi Dehaghi Z. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges. J Med Syst. 2014; 38: 110. |
| [117] | Runchal A. K., Rao M. M. (2020) CFD of the Future: Year 2025 and Beyond. In: Runchal A. (eds) 50 Years of CFD in Engineering Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-15-2670-1_22. |
| [118] | KukrejaS A comprehensive study on the applications of artificial intelligence for the medical diagnosis and prognosis of asthma. [Cited 2019 Jul 16]. Available from SSRN: https://ssrn.com/abstract=3081746. |
| [119] | Jorge L. M. Amaral, Agnaldo J. Lopes, Jose M. Jansen, Alvaro C. D. Faria, Pedro L. Melo, Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, computer methods and programs in biomedicine. 105 (2012) 183-193. dio: 10.1016/cmpb.2011.09.009. |
| [120] | Jorge L. M. Amaral, Agnaldo J. Lopes, Juliana Veiga, Alvaro C. D. Faria, and Pedro L. Melo. 2017. High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements. Comput. Methods Prog. Biomed. 144, C (June 2017), 113–125. DOI: https://doi.org/10.1016/j.cmpb.2017.03.023. |
| [121] | Elsa Angelini, Simon Dahan and Anand Shah (2019). Unravelling machine learning: insights in respiratory medicine; European Respiratory Journal. https://doi.org/10.1183/13993003.01216- 2019. vol. 54 no. 6.1399-3003. |
| [122] | Alan Kaplan, Hui Cao, J. Mark FitzGerald, Nick Iannotti, Eric Yang, Janwillem W. H. Kocks, Konstantinos Kostikas, David Price, Helen K. Reddel, Ioanna T siligianni, Claus F. Vogelmeier, Pascal P. fister, Paul Mastoridis, Pharm D (2021). Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. DOI: https://doi.org/10.1016/j.jaip.2021.02.014 The journal of allergy and clinical immunology. |
APA Style
Oyejide James Ayodele, Atoyebi Ebenezer Oluwatosin, Olutosoye Christian Taiwo, Ademola Adebukola Dare. (2021). Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects. International Journal of Biomedical Science and Engineering, 9(2), 16-26. https://doi.org/10.11648/j.ijbse.20210902.12
ACS Style
Oyejide James Ayodele; Atoyebi Ebenezer Oluwatosin; Olutosoye Christian Taiwo; Ademola Adebukola Dare. Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects. Int. J. Biomed. Sci. Eng. 2021, 9(2), 16-26. doi: 10.11648/j.ijbse.20210902.12
AMA Style
Oyejide James Ayodele, Atoyebi Ebenezer Oluwatosin, Olutosoye Christian Taiwo, Ademola Adebukola Dare. Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects. Int J Biomed Sci Eng. 2021;9(2):16-26. doi: 10.11648/j.ijbse.20210902.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijbse.20210902.12,
author = {Oyejide James Ayodele and Atoyebi Ebenezer Oluwatosin and Olutosoye Christian Taiwo and Ademola Adebukola Dare},
title = {Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects},
journal = {International Journal of Biomedical Science and Engineering},
volume = {9},
number = {2},
pages = {16-26},
doi = {10.11648/j.ijbse.20210902.12},
url = {https://doi.org/10.11648/j.ijbse.20210902.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20210902.12},
abstract = {Breathing conditions pertaining to nasal obstruction, obstructive sleep apnea, and airflow resistance in the human lower airways have been investigated extensively by researchers over the years. Due to the availability of advanced computer numerical models, such as computational fluid dynamics (CFD), researchers have made progressive studies of airflow characteristic, especially the effects of airflow pressure, velocity and wall shear stress in human obstructive airways. Studies utilizing CFD have enhanced clinical understanding of the physiology and pathophysiology of the respiratory system through the concept of three-dimensional models that facilitate airflow simulation. The main objective of this article is to review recent CFD literature on nasal airflow and lower airway obstruction. The review covers the role of segmentation threshold in the outcome of airflow simulation in the nasal cavity, and results of fluid structure interaction (FSI) and computational fluid dynamics in nasal obstruction and airway collapse in obstructive sleep apnea were also correlated. For models of the lower airways, we evaluated the effect of extra-thoracic airway (ETA) on downstream airflow during simulation against the popular Weibel’s model. In the concluding section, we discussed the advantages, limitations, and prospects (precisely with deep machine learning) of computational fluid dynamics in the clinical assessment and investigation of respiratory diseases.},
year = {2021}
}
TY - JOUR T1 - Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: Current Applications and Prospects AU - Oyejide James Ayodele AU - Atoyebi Ebenezer Oluwatosin AU - Olutosoye Christian Taiwo AU - Ademola Adebukola Dare Y1 - 2021/05/15 PY - 2021 N1 - https://doi.org/10.11648/j.ijbse.20210902.12 DO - 10.11648/j.ijbse.20210902.12 T2 - International Journal of Biomedical Science and Engineering JF - International Journal of Biomedical Science and Engineering JO - International Journal of Biomedical Science and Engineering SP - 16 EP - 26 PB - Science Publishing Group SN - 2376-7235 UR - https://doi.org/10.11648/j.ijbse.20210902.12 AB - Breathing conditions pertaining to nasal obstruction, obstructive sleep apnea, and airflow resistance in the human lower airways have been investigated extensively by researchers over the years. Due to the availability of advanced computer numerical models, such as computational fluid dynamics (CFD), researchers have made progressive studies of airflow characteristic, especially the effects of airflow pressure, velocity and wall shear stress in human obstructive airways. Studies utilizing CFD have enhanced clinical understanding of the physiology and pathophysiology of the respiratory system through the concept of three-dimensional models that facilitate airflow simulation. The main objective of this article is to review recent CFD literature on nasal airflow and lower airway obstruction. The review covers the role of segmentation threshold in the outcome of airflow simulation in the nasal cavity, and results of fluid structure interaction (FSI) and computational fluid dynamics in nasal obstruction and airway collapse in obstructive sleep apnea were also correlated. For models of the lower airways, we evaluated the effect of extra-thoracic airway (ETA) on downstream airflow during simulation against the popular Weibel’s model. In the concluding section, we discussed the advantages, limitations, and prospects (precisely with deep machine learning) of computational fluid dynamics in the clinical assessment and investigation of respiratory diseases. VL - 9 IS - 2 ER -
Email: