BACKGROUND: Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access soft...
BACKGROUND: The facial landmark annotation of 3D facial images is crucial in clinical orthodontics and orthognathic surgeries for accurate diagnosis and treatment planning. While manual landmarking has traditionally been the gold standard, it is labo...
OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and thre...
BACKGROUND: Cone-beam computed tomography (CBCT) has several applications in various fields of dental medicine such as diagnosis and treatment planning. When compared to computed tomography (CT), CBCT's radiation exposure dose is decreased by 3%-20%....
AIM: This study was aimed to evaluate two artificial intelligence (AI) systems that created a prioritized problem list and treatment plan, and examine whether the performance of the aforementioned systems was equivalent to orthodontists.
OBJECTIVES: The aim of the study was to evaluate the accuracy of a cascaded two-stage convolutional neural network (CNN) model in detecting upper airway (UA) soft tissue landmarks in comparison with the skeletal landmarks on the lateral cephalometric...
OBJECTIVES: To evaluate facial attractiveness of treated cleft patients and controls by artificial intelligence (AI) and to compare these results with panel ratings performed by laypeople, orthodontists, and oral surgeons.