Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks.

Material And Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review.

Radiographic alveolar bone assessment in correlation with primary implant stability: A systematic review and meta-analysis.

Introduction: The radiographic examination of alveolar bone using 3D radiographic examination is essential in dental implant treatment planning. Our study aimed to systematically review and quantitatively analyze the correlation between alveolar bone parameters, specifically bone density and cortical bone thickness, assessed using cone beam computed tomography (CBCT) and/or multidetector computed tomography (MDCT); and primary implant stability (PIS) determined using implant stability quotient (ISQ), Periotest® value (PTV), and insertion torque value (ITV).

Methods: This review was registered in the PROSPERO database (registration number CRD42022307245).

Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach.

Objective: This study aimed to assess the performance of the deep learning (DL) model for automated tooth numbering in panoramic radiographs.

Study Design: The dataset of 500 panoramic images was selected according to the inclusion criteria and divided into training and testing data with a ratio of 80%:20%. Annotation on the data set was categorized into 32 classes based on the dental nomenclature of the universal numbering system using the LabelImg software.