Computational Fluid Dynamics Modeling in Respiratory Airways Obstruction: <i>Current Applications and Prospects</i>

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.

Computational Fluid Dynamics and Its Potential Applications for the ENT Clinician

“…Assessing heat flux helps identify areas where temperature changes can have a substantial impact on tissue healthusually when extreme conditions arise after nasal surgery. 18 Mucosal cooling has been investigated 19 and has shown affect perceived nasal patency and may have significance in clinical evaluation.…”

Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

“…Depending on its density, bone is represented by HU in the range 300 to 2500. There is no consensus about the appropriate level to describe the air–tissue interface (e.g., the mucous layer), and the literature reports HU levels used in the range −800 to −300 [ 99 ].…”

“…Radulesco et al [ 190 ] gave a literature review focusing on the usefulness of CFD in the assessment of NAO. Other recent literature reviews were given by, e.g., Faizal et al [ 191 ] and Ayodele et al [ 99 ]. Recent publications considering patient-specific mathematical modelling and simulation for clinical decision support include, e.g., [ 89 , 90 , 192 , 193 , 194 , 195 ].…”

Biomedical Engineering Education in Nigeria

“…Furthermore, an exploratory literature review on BME research in Nigeria points to significant efforts in the areas of biomaterials, biomechanics, cardiovascular and biofluid mechanics, bio instrumentation, the physical and mechanical behaviour of tissues treated on engineering materials, artificial intelligence, implantable devices and finite element models (Okorie, 2015). Research has been conducted on a diagnostic tool for obesity related issues (Vincent et al, 2019;Owolabi et al, 2021;Jilantikiri et al, 2022), rehabilitation (Fidelis and Arowolo, 2023), respiratory and cardiovascular biomechanics (Ayodele et al, 2021;, biomaterials and tissue engineering (Ebenezer et al, 2023;Joy et al, 2022), bioinstrumentation (Yahaya et al, 2019), medical artificial intelligence Nwaneri and Ugo, 2022;, healthcare technology ) and biofluid mechanics (Ayodele et al, 2022;Fetuga et al, 2023).…”