Surgery

Computed tomography

Pharynx

Computer science

Sørensen–Dice coefficient

Cone beam computed tomography

Image segmentation

Radiology

Airway

Segmentation

Anatomy

Pattern recognition (psychology)

Artificial intelligence

Medicine

Deep learning

Convolutional neural network

Computer vision

Orthodontics and Craniofacial Research

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 intelligence (AI) system. Setting and Sample Population Archives of the CBCT images were reviewed, and the data of 306 subjects with the pharyngeal airway were included in this retrospective study. Material and Methods A machine learning algorithm, based on Convolutional Neural Network (CNN), did the segmentation of the pharyngeal airway on serial CBCT images. Semi‐automatic software (ITK‐SNAP) was used to manually generate the airway, and the results were compared with artificial intelligence. Dice similarity coefficient (DSC) and Intersection over Union (IoU) were used as the accuracy of segmentation in comparing the measurements of human measurements and artificial intelligence algorithms. Results The human observer found the average volume of the pharyngeal airway to be 18.08 cm 3 and artificial intelligence to be 17.32 cm 3 . For pharyngeal airway segmentation, a dice ratio of 0.919 and a weighted IoU of 0.993 is achieved. Conclusions In this study, a successful AI algorithm that automatically segments the pharyngeal airway from CBCT images was created. It can be useful in the quick and easy calculation of pharyngeal airway volume from CBCT images for clinical application.

A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images