Generating synthetic CT images from unpaired head and neck CBCT images and validating the importance of detailed nasal cavity acquisition through simulations.

Background And Objective: Computed tomography (CT) of the head and neck is crucial for diagnosing internal structures. The demand for substituting traditional CT with cone beam CT (CBCT) exists because of its cost-effectiveness and reduced radiation exposure. However, CBCT cannot accurately depict airway shapes owing to image noise.

Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts.

This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images.

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach.

Background: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography.

Objectives: To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.