Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to determine anatomical features which have a large influence on airflow through a combination of Computational Fluid Dynamics (CFD) and machine learning method. Firstly, the aerodynamic characteristics of the nasal vestibule are detailedly analyzed using the CFD method. Based on CFD simulation results, we divide the nasal vestibule into two types with distinctly different airflow patterns, which is consistent with clinical findings. Secondly, we explore the relationship between anatomical features and aerodynamic characteristics by developing a novel machine learning model which could predict airflow patterns based on several anatomical features. Feature mining is performed to determine the anatomical feature which has the greatest impact on respiratory function. The method is developed and validated on 41 unilateral nasal vestibules from 26 patients with nasal obstruction. The correctness of the CFD analysis and the developed model is verified by comparing them with clinical findings.
Keywords: Anatomical feature; CFD; Machine learning; Nasal vestibule.
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