OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).
OBJECTIVE(S): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated ...
Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized de...
Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of ...
BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric ...
OBJECTIVE: This study aimed to evaluate the effectiveness of Dental Monitoring™ (DM™) Artificial Intelligence Driven Remote Monitoring Technology (AIDRM) technology in improving the patient's oral hygiene during orthodontic treatment through AI-based...
There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of...
OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that ...
OBJECTIVE: The aim of this study is to evaluate whether fully automatic cephalometric analysis software with artificial intelligence algorithms is as accurate as non-automated cephalometric analysis software for clinical diagnosis and research.
OBJECTIVE: The aim of this study was to develop an artificial intelligence (AI) algorithm to automatically and accurately determine the stage of cervical vertebra maturation (CVM) with the main purpose being to eliminate the human error factor.