OBJECTIVE: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres.
OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.
OBJECTIVE: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-b...
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.