Dental Health

Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis.

Lateral cephalograms and related analysis constitute representative methods for orthodontic treatment. However, since conventional cephalometric radiographs display a three-dimensional structure on a two-dimensional plane, inaccuracies may be produced when quantitative evaluation is required. Cone-beam computed tomography (CBCT) has minimal image distortion, and important parts can be observed wit...

Neural network ensemble model for prediction of erythrocyte sedimentation rate (ESR) using partial least squares regression.

The erythrocyte sedimentation rate (ESR) is a non-specific blood test for determining inflammatory conditions. However, the long measurement time (60 min) to obtain ESR is an obstacle for a prompt evaluation. In this study, to reduce the measurement time of ESR, deep neural networks (DNNs) were applied to the sedimentation tendency of blood samples. DNNs using multilayer perceptron (MLP), long sho...

Quantification and comparison of the regional acceleratory phenomenon in bone following piezosurgery or bur osteotomy: A pilot study in rats.

BACKGROUND/OBJECTIVE: The Regional Acceleratory Phenomenon (RAP) can be induced surgically via decortication (selective cortical penetrations) of bone...

Electrostatic Adhesion Clutch with Superhigh Force Density Achieved by MXene-Poly(Vinylidene Fluoride-Trifluoroethylene-Chlorotrifluoroethylene) Composites.

Electrostatic adhesion (EA) clutches are widely applied in robots, wearable devices, and virtual reality, due to their compliance, lightweight, ultrat...

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic t...

Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos.

BACKGROUND: Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagn...

Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to analyze the accuracy of artificial intelligence (AI) for orthodontic tooth extraction decision-making.

Automated detection of smiles as discrete episodes.

BACKGROUND: Patients seeking restorative and orthodontic treatment expect an improvement in their smiles and oral health-related quality of life. None...

Present status and future directions: Imaging techniques for the detection of periapical lesions.

Diagnosing and treating apical periodontitis (AP) in an attempt to preserve the natural dentition, and to prevent the direct and indirect systemic eff...

A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss.

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive a...

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis...

iCVM: An Interpretable Deep Learning Model for CVM Assessment Under Label Uncertainty.

The Cervical Vertebral Maturation (CVM) method aims to determine the craniofacial skeletal maturational stage, which is crucial for orthodontic and or...

Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis.

AIM: The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodon...

Application of deep machine learning for the radiographic diagnosis of periodontitis.

OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of ...

Deep learning in periodontology and oral implantology: A scoping review.

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal an...

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs.

Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, mali...

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the...

Deep Learning-Based Prediction of the 3D Postorthodontic Facial Changes.

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 di...

Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review.

Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then t...

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic ...