Dental Health

Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.

OBJECTIVE: This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodontic treatment and to compare the fabrication process of integrated tooth models (ITMs) with manual method.

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation.

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different ra...

A two-stage deep learning architecture for radiographic staging of periodontal bone loss.

BACKGROUND: Radiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, i...

Artificial Intelligence for Classifying and Archiving Orthodontic Images.

One of the main requirements for orthodontic treatment is continuous image acquisition. However, the conventional system of orthodontic image acquisit...

Inhibition of peptidyl arginine deiminase, a virulence factor, by antioxidant-rich : and evaluation.

, the cause of periodontitis, is also linked to many systemic disorders due to its citrullination capability from a unique peptidyl arginine deiminase...

Automatic detection of anteriorly displaced temporomandibular joint discs on magnetic resonance images using a deep learning algorithm.

OBJECTIVES: This study aimed to develop models that can automatically detect anterior disc displacement (ADD) of the temporomandibular joint (TMJ) on ...

Association of Gastroesophageal Reflux Disease with Preterm Birth: Machine Learning Analysis.

BACKGROUND: This study used machine learning and population data for testing the associations of preterm birth with gastroesophageal reflux disease (G...

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics.

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning under...

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning.

OBJECTIVES: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluat...

Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients.

From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orth...

Artificial intelligence in orthodontics: Where are we now? A scoping review.

OBJECTIVE: This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthod...

Clinically applicable artificial intelligence system for dental diagnosis with CBCT.

In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anat...

mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors.

Ensuring that all the teeth surfaces are adequately covered during daily brushing can reduce the risk of several oral diseases. In this paper, we prop...

Automating Periodontal bone loss measurement via dental landmark localisation.

PURPOSE: Periodontitis is the sixth most prevalent disease worldwide and periodontal bone loss (PBL) detection is crucial for its early recognition an...

Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care.

The last decade (2010-2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic...

Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.

OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sampl...

Machine learning and orthodontics, current trends and the future opportunities: A scoping review.

INTRODUCTION: In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology ...

Quantitating the art and science of esthetic clinical success.

BACKGROUND: Beginning with the biobehavioral bases of esthetic experiences, this article presents a quantitative analytic review of the motives and me...

Machine Learning with F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction.

Coronary F-sodium fluoride (F-NaF) PET and CT angiography-based quantitative plaque analysis have shown promise in refining risk stratification in pat...

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic...