AIMC Topic: Cephalometry
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Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews.
Journal of dentistry
May 8, 2024
OBJECTIVES: The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) perf...
Artificial intelligence as a prediction tool for orthognathic surgery assessment.
Orthodontics & craniofacial research
May 7, 2024
INTRODUCTION: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodon...
Artificial Intelligence in Orthodontics: Critical Review.
Journal of dental research
Apr 29, 2024
With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluatio...
Mandibular and dental measurements for sex determination using machine learning.
Scientific reports
Apr 26, 2024
The present study tested the combination of mandibular and dental dimensions for sex determination using machine learning. Lateral cephalograms and dental casts were used to obtain mandibular and mesio-distal permanent teeth dimensions, respectively....
Sex estimation from maxillofacial radiographs using a deep learning approach.
Dental materials journal
Apr 11, 2024
The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed....
Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 20, 2024
OBJECTIVES: Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimat...
Deep learning for automatic detection of cephalometric landmarks on lateral cephalometric radiographs using the Mask Region-based Convolutional Neural Network: a pilot study.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 12, 2024
OBJECTIVE: We examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs).
Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.
Orthodontics & craniofacial research
Feb 6, 2024
OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).
State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis.
Journal of stomatology, oral and maxillofacial surgery
Jan 30, 2024
OBJECTIVE: To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery.
Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners.
Oral surgery, oral medicine, oral pathology and oral radiology
Jan 22, 2024
OBJECTIVE: To assess the accuracy and reproducibility of cephalometric landmark identification performed by 2 artificial intelligence (AI)-driven applications (CefBot and WebCeph) and human examiners.
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