Upper airways modelling and validation of mandibular advancement surgery

Area of Science:

• Biomedical Engineering
• Fluid Dynamics
• Medical Physics

Background:

• Obstructive Sleep Apnoea (OSA) diagnosis relies on upper airway (UA) cross-sectional analysis.
• Current assessment methods for OSA are limited, and surgical interventions can be detrimental.
• There is a critical need for improved diagnostic and predictive tools for OSA.

Purpose of the Study:

• To integrate Computational Fluid Dynamics (CFD) with physical model validation for enhanced Obstructive Sleep Apnoea (OSA) prediction.
• To improve the accuracy and reliability of turbulence models in upper airway (UA) simulations.
• To validate CFD simulations against physical models for improved OSA assessment.

Main Methods:

• Utilized the k-omega SST turbulence model for CFD analysis of the upper airway (UA).
• Constructed a physical model of the UA using Selective Laser Sintering (SLS) for CFD simulation.
• Performed cross-validation between the physical UA model and CFD turbulence simulations.

Main Results:

• Observed a significant decrease in average UA pressure differential post-mandibular advancement surgery.
• Detected an increase in Turbulent Kinetic Energy (TKE) after surgery, indicating heightened turbulence.
• Confirmed the validity of CFD turbulence simulations through physical model cross-validation.

Conclusions:

• Matching UA simulations with physical models significantly improves OSA assessment accuracy.
• CFD, when validated with physical models, offers a reliable method for evaluating OSA therapies, particularly surgical outcomes.
• Further research is required to understand the implications of increased TKE post-surgery on OSA treatment effectiveness.