Comparison of rhinomanometric and computational fluid dynamic assessment of nasal resistance with respect to measurement accuracy

The influence of flowmeters on rhinomanometry results and detection of nasal airflow asymmetry

<b><br>Introduction:</b> Rhinomanometry is an otolaryngological diagnostic method used to determine airflow as a function of the pressure drop through the left and right nasal cavities. Airflow is measured using orifice flowmeters that attenuate the flow.</br> <b><br>Aim:</b> This paper describes the results of a study into the effects of flowmeter design on rhinomanometry results and detection of nasal airflow asymmetry.</br> <b><br>Material and methods:</b> Four flowmeters were examined using a 3D printed model of a human nose.</br> <b><br>Conclusions:</b> Each flowmeter interfered with the rhinomanometry results.</br>.

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

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

Accuracy of virtual rhinomanometry

Introduction: This paper describes the results of research aimed at developing a method of otolaryngological diagnosis based on computational fluid dynamics, which has been called Virtual Rhinomanometry.

Material and methods: Laboratory studies of airflows through a 3D printed model of nasal cavities based on computed tomography image analysis have been performed. The CFD results have been compared with those of an examination of airflow through nasal cavities (rhinomanometry) of a group of 25 patients.

Results: The possibilities of simplifying model geometry for CFD calculations have been described, the impact of CT image segmentation on geometric model accuracy and CFD simulation errors have been analysed, and recommendations for future research have been described.

Conclusions: The measurement uncertainty of the nasal cavities’ walls has a significant impact on CFD simulations. The CFD simulations better approximate RMM results of patients after anemization, as the influence of the nasal mucosa on airflow is then reduced. A minor change in the geometry of the nasal cavities (within the range of reconstruction errors by CT image segmentation) has a major impact on the results of CFD simulations.

Computational Fluid Dynamics Could Enable Individualized Surgical Treatment of Nasal Obstruction (A Preliminary Study)

Passage of nasal airflow during breathing is crucial in achieving accurate diagnosis and optimal therapy for patients with nasal disorders. Computational fluid dynamics (CFD) is the dominant method for simulating and studying airflow. The present study aimed to create a CFD nasal airflow model to determine the major routes of airflow through the nasal cavity and thus help with individualization of surgical treatment of nasal disorders. The three-dimensional nasal cavity model was based on computed tomography scans of the nasal cavity of an adult patient without nasal breathing problems. The model showed the main routes of airflow in the inferior meatus and inferior part of the common meatus, but also surprisingly in the middle meatus and in the middle part of the common nasal meatus. It indicates that the lower meatus and the lower part of the common meatus should not be the only consideration in case of surgery for nasal obstruction in our patient. CFD surgical planning could enable individualized precise surgical treatment of nasal disorders. It could be beneficial mainly in challenging cases such as patients with persistent nasal obstruction after surgery, patients with empty nose syndrome, and patients with a significant discrepancy between the clinical findings and subjective complaints.