Evaluation of Alveolar Bone Destruction Patterns in the Posterior Region of the Maxilla Through Cone Beam Computer Tomography on 361 Consecutive Patients: Effect of Age and Gender.

Objectives: The aim of this retrospective study is to evaluate the effect of age and gender on the alveolar bone destruction pattern with cone beam computed tomography (CBCT) in the posterior region of the maxilla.

Materials And Methods: The study group included CBCT image records of 361 consecutive patients (180 males and 181 females) aged 20 years and older. Alveolar crest morphology in the maxillary right and left first and second molar teeth on retrospective images was classified as a horizontal or vertical defect (one-walled, two-walled, three-walled, and combined bone defect) on four surfaces (mesial, distal, buccal, and palatinal).

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study.

Introduction: Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images.

Materials And Methods: Oral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic.

A Deep Learning Approach for Masseter Muscle Segmentation on Ultrasonography.

Aim: Deep learning algorithms have lately been used for medical image processing, and they have showed promise in a range of applications. The purpose of this study was to develop and test computer-based diagnostic tools for evaluating masseter muscle segmentation on ultrasonography images.

Materials And Methods: A total of 388 anonymous adult masseter muscle retrospective ultrasonographic images were evaluated.

A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study.

Introduction: Deep learning methods have recently been applied for the processing of medical images, and they have shown promise in a variety of applications. This study aimed to develop a deep learning approach for identifying oral lichen planus lesions using photographic images.

Material And Methods: Anonymous retrospective photographic images of buccal mucosa with 65 healthy and 72 oral lichen planus lesions were identified using the CranioCatch program (CranioCatch, Eskişehir, Turkey).

A systematic review and meta-analysis on serum and salivary levels of total antioxidant capacity and C-reactive protein in oral lichen planus patients.

Objective: Oral lichen planus (OLP) is an inflammatory disease that total antioxidant capacity (TAC) and C-reactive protein (CRP) play an important role in its pathogenesis. In this meta-analysis, we compared the salivary and serum levels of TAC and CRP between OLP patients and controls.

Design: A comprehensive search was performed in four databases (PubMed, Scopus, Web of Science, and Cochrane Library).