Comparison of artificial intelligence junior dentists' diagnostic performance based on caries and periapical infection detection on panoramic images.

Background: There is information missing in the literature about the comparison of dentists artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists.

Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds.

Deep learning for diagnostic charting on pediatric panoramic radiographs.

Aim: Artificial intelligence (AI)-based systems are used in dentistry to ensure a more accurate and efficient diagnostic process. The objective of the present study was to evaluate the performance of a deep learning (DL) program for the detection and classification of dental structures and treatments on panoramic radiographs of pediatric patients.

Materials And Methods: In total, 4821 anonymized digital panoramic radiographs of children between 5 and 13 years of age were analyzed by YOLOv4, a CNN (Convolutional Neural Networks)-based object detection model.

A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs.

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs.

Materials And Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs.

Bioactivity evaluation of calcium silicate-based endodontic materials used for apexification.

This study aims to compare the bioactivity of Biodentine, ProRoot MTA and NeoMTA Plus with regard to their element uptake (Ca, Si and Ca/P) by root canal dentine in a simulated apex (n = 30 each) and evaluate the correlation between the dentine fracture resistance (n = 30 each) and interfacial layer thickness. Specimens immersed in a corrected simulated body solution (c-SBF) for 1, 30 and 90 days were used. In all test materials, the Ca and Si concentrations in the root dentine were found to be significantly higher, whereas the Ca/P and Si concentrations increased over time (P < 0.