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.

Evaluating trabecular microstructure of mandible and axis in osteoporosis, diabetes and chronic kidney disease using cone beam computed tomography.

Objectives: Like other bones, the mandible and cervical vertebrae are affected by several systemic diseases. The aim of this study is to evaluate the effects of osteoporosis (OP), diabetes mellitus (DM), and dialysis-indicated advanced chronic kidney disease (CKD), which are the most effective systemic diseases on the bone metabolism, on the trabecular microstructure of the mandible and cervical vertebrae using cone beam computed tomography (CBCT).

Methods: 81 patients who signed our informed consent form are involved in the study.

Assessment of prevalence and volumetric estimation of possible Stafne bone concavities on cone beam computed tomography images.

Objectives: Our study aimed to determine the prevalence and volumetric estimates of Stafne bone cavities (SBC) on cone beam computed tomography (CBCT) images.

Methods: This retrospective study, which involved the CBCT images of 1141 men and 1260 women with an age range of 10-90 years, aimed to determine the prevalence of SBCs and to calculate their volumes using the semi-automatic segmentation method.

Results: A total of 15 SBCs were diagnosed in 14 patients.