Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.

Objective: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.

Methods: In this study, a total of 546 IMMs from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.

Comparative Morphometric Study of the Occipital Condyle in Class III and Class I Skeletal Malocclusion Patients.

Objectives: Since the formation of skeletal malocclusions is closely linked to general craniofacial development, it is crucial to understand the anatomy and growth patterns of the skull base. This study aimed to assess the morphometry of the occipital condyle (OC) on CBCT scans of Class III skeletal malocclusion subjects and compare the findings with those of skeletal Class I malocclusion subjects.

Methods: A retrospective analysis was performed on CBCT images based on predefined inclusion and exclusion criteria.

The detection of distomolar teeth on panoramic radiographs using different artificial intelligence models.

Purposes: One notable anomaly, presence of distomolars, arises beyond the typical sequence of the human dental system. In this study, convolutional neural networks (CNNs) based machine learning methods were employed to classify distomolar tooth existence using panoramic radiography (PR).

Methods: PRs dataset, composed of 117 subjects with distomolar teeth and 146 subjects without distomolar teeth, was constructed.

Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.

Background: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imaging methods can be used.

Aim: The aim of this study was to evaluate the diagnostic performance of deep learning (DL) applications that assess the relationship of the MSF to the first maxillary molar teeth (fMMT) and second maxillary molar teeth (sMMT) on PRs with data confirmed by cone beam computed tomography (CBCT).