OBJECTIVES: This retrospective in vitro study evaluated the impact of input data quantity on the morphology of dental crowns generated by AI-based software. The hypothesis suggests that increased input data quantity improves the quality of generated ...
BACKGROUND: Robotics in endodontics enhances precision, efficiency, and treatment success through AI, haptic feedback, and autonomous systems. Despite its potential, adoption is limited by cost, accessibility limitations, and training barriers. This ...
OBJECTIVES: This clinical study aimed to compare the accuracy of implant placement obtained using a robotic system and a full-guide template in patients with dentition defects.
OBJECTIVES: Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodo...
OBJECTIVES: Artificial intelligence (AI) is increasingly being integrated into intraoral scanners (IOS) to improve the quality of digital impressions. However, information on the accuracy of AI-assisted virtual models is limited. This study aimed to ...
OBJECTIVES: The graded diagnosis of periodontitis has always been a difficulty for dentists. This systematic review aimed to investigate the performance of artificial intelligence (AI) models for periodontitis classification.
OBJECTIVES: Considering Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network approaches have shown promising image classification performance, the aim of this study was to compare the performance of novel Convolutional Neural ...
OBJECTIVES: To develop and validate an explainable Artificial Intelligence (AI) model for classifying and quantifying upper airway obstruction related to adenoid hypertrophy using three-dimensional (3D) shape analysis of cone-beam computed tomography...
OBJECTIVES: Incorporating artificial intelligence (AI) in assessing dental students' knowledge and skills is in its infancy, despite AI being well established as an aid to aspects of clinical diagnosis and education. This study aimed to investigate w...
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.