Evaluation of Dental Plaque Area with Artificial Intelligence Model.

Objectives: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth.

Materials And Methods: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent.

Evaluation of knowledge and awareness about teledentistry among dentists and patients living in Turkey.

Background: Teledentistry is a field of telemedicine that combines digital technology and clinical dentistry, enabling remote communication between dentists and patients.

Objectives: The aim of the present study was to evaluate the knowledge and awareness of dentists and patients about teledentistry in Turkey.

Material And Methods: This cross-sectional study was conducted among general and specialist dentists in Turkey, and dental patients in Edirne, Turkey.

Effect of three different remineralising agents on prevention against acidic erosion of primary teeth: an in vitro study.

Purpose: This study aimed to evaluate and compare the protective effect of fluoride varnish (Enamelast™, Ultradent Inc., Cologne, Germany), casein phosphopeptide-amorphous calcium phosphate fluoride/CPP-ACPF (MI Paste Plus, GC Corp., Tokyo, Japan) and self-assembling P peptide (Curodont™ Protect, Credentis AG, Windisch, Switzerland), against acidic erosion of primary teeth.

Effectiveness of an educational cartoon animation on the knowledge of children about traumatic dental injuries.

Background/aim: Although children are frequently exposed to traumatic dental injuries (TDIs), their knowledge and attitude regarding the emergency management of TDIs are largely insufficient. The aim of this study was to determine the knowledge of children about TDIs utilizing a questionnaire before and after watching a custom animated instructional video.

Material And Methods: The study was conducted with 332 children aged 8 to 13 years.

Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network.

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance.

Materials And Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images.