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.

Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.

Objectives: This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists.

Methods: A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet.

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).

Assessment of Knowledge Level and Awareness About Human Papillomavirus Among Dental Students.

Epidemiological studies have shown that the worldwide trend of human papillomavirus (HPV)-induced oral cancer has increased. Dentistry students need comprehensive information about HPV to provide accurate advice to their patients. The aim of this study is to evaluate 3rd, 4th, and 5th grade students' knowledge and awareness about HPV.

Assessment of Masseter Muscle Appearance and Thickness in Edentulous and Dentate Patients by Ultrasonography.

Objectives: The purpose of the present study was to examine ultrasonographic appearances of Masseter Muscle (MM) in dentate and edentulous patients without Temporomandibular Disorder (TMD).

Materials And Methods: The thickness of the MM in 25 dentate (mean age: 30,68 ± 10,49) and 24 edentulous (mean age: 61,46 ± 9,71) patients, who visited routine dental examination, was measured at rest and at maximum contraction bilaterally. Examinations were performed using an Aloka Prosound α6 (Hitachi Aloka Medical Systems, Tokyo, Japan) equipped with an 8 MHz-wide bandwidth linear active matrix transducer (ranging from 1 to 15 MHz).

Assessing Oral Cancer Awareness Among Dental Students.

The aim of this study was to assess oral cancer awareness among undergraduate dental students in Marmara University Faculty of Dentistry. A validated questionnaire which tested oral cancer awareness was given to third- and fifth-year students of the dental faculty of Marmara University. A total of 198 students participated in this survey.

Awareness assessment in Turkish subpopulation with chronic oral mucosal diseases.

Objectives: The aim of this study was to evaluate the awareness of group Turkish patients with chronic oral mucosal diseases by chronic oral mucosal diseases questionnaires (COMDQ).

Materials And Methods: Eighty patients with chronic oral mucosal diseases were participated in the study. A detailed medical history of each patient was taken, and all the COMDQ questions, which were translated from English version, were filled out.