Predicted airway obstruction distribution based on dynamical lung ventilation data: A coupled modeling-machine learning methodology.

In asthma and chronic obstructive pulmonary disease, some airways of the tracheobronchial tree can be constricted, from moderate narrowing up to closure. Those pathological patterns of obstructions affect the lung ventilation distribution. While some imaging techniques enable visualization and quantification of constrictions in proximal generations, no noninvasive technique exists to provide the airway morphology and obstruction distribution in distal areas. In this work, we propose a method that exploits lung ventilation measures to access positions of airway obstructions (restrictions and closures) in the tree. This identification approach combines a lung ventilation model, in which a 0D tree is strongly coupled to a 3D parenchyma description, along with a machine learning approach. On the basis of synthetic data generated with typical temporal and spatial resolutions as well as reconstruction errors, we obtain very encouraging results of the obstruction distribution, with a detection rate higher than 85%.