Early Career Researcher: Student (poster and 2-minute poster pitch) at Digital Twin Symposium 2019, presented by Omid Bafkar
Accurate prediction of upper airway deformation and collapse during breathing is required for effective and personalised treatment of obstructive sleep apnoea (OSA). While numerical modelling techniques such as fluid-structure interaction (FSI) are promising, it remains a challenge to predict the deformation of the airway accurately during breathing, and thus the occurrence of OSA. These difficulties arise because the structural properties of soft tissue in the human upper airway vary due to neuromuscular effects on muscle relaxation. Additionally, both the elasticity and anisotropy of the soft tissues along the upper airway are poorly characterised. Finally, gravitational effects on anatomic features have been omitted from prior studies.
Materials and methods:
A novel FSI technique is presented here that provides detailed information on how different sleeping positions and the associated variation in gravitational force affects the upper airway & causes it to collapse. Due to the uncertainty in the material properties of the upper airway's soft tissues, the effect of the material stiffness of the tongue base and the soft palate studied. Three distinct values were employed, which demonstrate the relationship between material properties and the upper airway's collapse. Also, the effect of sleeping position on the upper airway's soft tissues by considering two sleeping positions, 'Supine' and 'Prone' investigated.
The results of this investigation show that three main factors affect collapse in the upper airway; (i) sleeping position, (ii) gravitational force, and (iii) stiffness of the material properties. These findings identify how virtual modelling can determine the location of the collapse and thus be used to determine the optimal treatment solution for OSA. The provided technique brings the possibility to measure and detect the level and exact location of the blockage in the upper airway for the diagnosed OSA patients and could be a great solution to improve the accuracy in the treatment stage.