Potential of computational models in personalized treatment of obstructive sleep apnea: a patient-specific partial 3D finite element study

Evaluation of mandibular advancement surgery efficacy in treating obstructive sleep apnea: A study on turbulence kinetic energy

BACKGROUND AND OBJECTIVE

Obstructive sleep apnoea (OSA) is a prevalent sleep disease characterised by recurrent airway obstruction during sleep, resulting in diminished oxygen intake and disrupted sleep patterns. This study investigates the effectiveness of mandibular advancement surgery as a surgical intervention for obstructive sleep apnoea by analysing the postoperative alterations in turbulence kinetic energy (TKE).

METHODOLOGY

The research involved five subjects receiving mandibular advancement surgery (MAS). The quantification of TKE was performed both before and throughout the method using a combination of computational fluid dynamics (CFD) models and empirical measurements. A suitable grid size of 2.6 million cells for CFD simulations was determined by grid sensitivity analysis and corroborated with physical measurements.

RESULTS

The findings indicated a significant increase in TKE for each individual post-procedure, with increments varying from 23 % to 460 %. The elevated TKE indicates a more rapid airflow in the upper airway post-surgery. This is probably attributable to alterations in the airway's morphology resulting from the surgery. The observed rise in speed and turbulence is theoretically supported by Bernoulli's principle, which elucidates the relationship between air flow velocity and the pressure it generates.

CONCLUSIONS

This study demonstrates that mandibular advancement surgery efficiently alleviates OSA by markedly enhancing airflow and diminishing turbulence in the upper airway post-treatment. The use of physical validation and grid sensitivity analysis in computational fluid dynamics simulations underscores the meticulous technique utilised, offering a comprehensive assessment of the efficacy of the surgical interventions for OSA.

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.