AI Medical Compendium Journal:
BMC oral health

Showing 111 to 120 of 130 articles

A population-based study to assess two convolutional neural networks for dental age estimation.

BMC oral health
BACKGROUND: Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of using artificial intelligence-based methods in an eastern Chines...

Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs.

BMC oral health
BACKGROUND: It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into norm...

Caries detection with tooth surface segmentation on intraoral photographic images using deep learning.

BMC oral health
BACKGROUND: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm fo...

A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth.

BMC oral health
BACKGROUND: Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar ...

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

BMC oral health
BACKGROUND: Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the...

Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically-a retrospective study.

BMC oral health
BACKGROUND: The purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bo...

Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images.

BMC oral health
OBJECTIVES: Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.

Robot and mechanical testing of a specialist manual toothbrush for cleaning efficacy and improved force control.

BMC oral health
BACKGROUND: Toothbrushes require flexibility to access all dental surfaces and remove plaque effectively, but they should also aim to prevent or limit overbrushing and consequent damage to teeth and gums. In two studies, the physical properties and c...

Prediction models for early diagnosis of actinomycotic osteomyelitis of the jaw using machine learning techniques: a preliminary study.

BMC oral health
BACKGROUND: This study aimed to develop and validate five machine learning models designed to predict actinomycotic osteomyelitis of the jaw. Furthermore, this study determined the relative importance of the predictive variables for actinomycotic ost...

Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform.

BMC oral health
BACKGROUND: Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric ...