Purpose: To develop and evaluate the accuracy of a computer-assisted system based on artificial intelligence for detecting and identifying dental implant brands using digital periapical radiographs.
Materials And Methods: A total of 1,800 digital periapical radiographs of dental implants from three distinct manufacturers (f1 = 600, f2 = 600, and f3 = 600) were split into training dataset (n = 1,440 [80%]) and testing dataset (n = 360 [20%]) groups. The images were evaluated by software developed by means of convolutional neural networks (CNN), with the aim of identifying the manufacturer of the dental implants contained in them.
Background: The aim of this study was to assess the anatomic aspects of the maxillary sinus septa, by means of computed tomography images, in a Brazilian population. The results might be of clinical significance in sinus lift surgery planning.
Material And Methods: In the study, 123 computed tomographs obtained from a private radiology clinic were used.
Objective: To conduct a morphometric evaluation of the incisive canal, adjacent structures, and their anatomic variations in Brazilian individuals.
Methods: A retrospective study was conducted using a sample of 157 multislice computed tomography images of adult Brazilian individuals of both sexes (20-96 years). The exam was performed with the RadiAnt DICOM Viewer 4.