Background: Despite advances in medicine and expenditures associated in treatment of nasal airway obstruction, 25-50% of patients undergoing nasal surgeries complain of persistent obstructive symptoms. Our objective is to develop a "stepwise virtual surgery" method that optimizes surgical outcomes for treatment of nasal airway obstruction.
Methods: Pre-surgery radiographic images of two subjects with nasal airway obstruction were imported into Mimics imaging software package for three-dimension reconstruction of the airway. A hierarchical stepwise approach was used to create seven virtual surgery nasal models comprising individual (inferior turbinectomy or septoplasty) procedures and combined inferior turbinectomy and septoplasty procedures via digital modifications of each subject's pre-surgery nasal model. To evaluate the effects of these procedures on nasal patency, computational fluid dynamics modeling was used to perform steady-state laminar inspiratory airflow and heat transfer simulations in every model, at resting breathing. Airflow-related variables were calculated for virtual surgery models and compared with dataset containing results of healthy subjects with no symptoms of nasal obstruction.
Findings: For Subject 1, nasal models with virtual septoplasty only and virtual septoplasty plus inferior turbinectomy on less obstructed side were within the healthy reference thresholds on both sides of the nasal cavity and across all three computed variables. For Subject 2, virtual septoplasty plus inferior turbinectomy on less obstructed side model produced the best result.
Interpretation: The hierarchical stepwise approach implemented in this preliminary report demonstrates computational fluid dynamics modeling ability to evaluate the efficiency of different surgical procedures for nasal obstruction in restoring nasal patency to normative level.
Keywords: 3D nasal models; Computational fluid dynamics (CFD) simulations; Nasal airway obstruction; Normative thresholds; Stepwise virtual surgery.
Copyright © 2018 Elsevier Ltd. All rights reserved.