Objectives Upper airway assessment requires a fully-automated segmentation system for
complete or sub-regional identification. This study aimed to develop a novel Deep Learning …

Objectives This study proposed and investigated the performance of a deep learning based
three-dimensional (3D) convolutional neural network (CNN) model for automatic …

Introduction This study aimed to test the accuracy of a new automatic deep learning–based
approach on the basis of convolutional neural networks (CNN) for fully automatic …

Objectives This study aims to evaluate an automatic segmentation algorithm for pharyngeal
airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial …

Introduction This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional
neural networks model for the fully automatic segmentation of regional pharyngeal volume …

Objective: To develop and validate convolutional neural network algorithms for automatic
upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two …

Objectives Orthodontic treatment profoundly impact the pharyngeal airway (PA) of patients.
Airway examination is an integral part of daily orthodontic diagnosis, and lateral …

Objective: To explore the effect of fully automatic image segmentation of adenoid and
nasopharyngeal airway by deep learning model based on U-Net network. Methods: From …

Background Cone-beam computed tomography (CBCT) has several applications in various
fields of dental medicine such as diagnosis and treatment planning. When compared to …

The structure of the upper airway is variable and complex due to its environmental and
physiological factors. Currently, doctors mainly rely on manual outline and segmentation …