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.

Automatic deep learning detection of overhanging restorations in bitewing radiographs.

Objectives: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.

Methods: A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels).

A potential novel technique for measurement of pulp volume on periapical radiography: A pilot study.

Pulp volume can be assessed during dental treatment. Three-dimensional imaging techniques are not routinely used for this purpose because of high radiation doses. This study aimed to develop a novel method to measure pulp volume using periapical radiography.

Micro-CT evaluation of the effect of various ScRp instrumentation methods on cement loss, porosity and micro-crack formation.

Objectives: The purpose of this study was to compare the effectiveness of three different instruments on cement loss, porosity and micro-crack formation, which was not evaluated before, following scaling and root planning (SRP) using micro-computed tomography (micro-CT).

Methods: In this experimental study, 30 single-rooted extracted human teeth were used and divided into three groups. All the teeth were scanned with micro-CT before and after SRP.

A Clinical Comparison of Er:YAG Laser, Piezosurgery, and Conventional Bur Methods in the Impacted Third Molar Surgery.

The aim of this study is to investigate whether Er:YAG laser and piezosurgery methods can be an alternative to the conventional bur method. The purpose of this study is to compare the postoperative pain, swelling, trismus and patient satisfaction between Er:YAG laser, piezosurgery device, and conventional bur methods that are used to remove bone barrier during extraction of the impacted lower third molar. Thirty healthy patients who have bilateral, asymptomatic, vertically impacted mandibular third molar teeth according to Pell and Gregory classification Class II and Winter Class B were selected.

Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography.

Objectives: This study aimed to determine mastoid emissary canal's (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences.

Methods: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views.

Using artificial intelligence models to evaluate envisaged points initially: A pilot study.

The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers.

Retrospective evaluation of the morpometric and morphological anatomy of the occipital condyle, hypoglossal canal and foramen magnum in Turkish populaton with CBCT.

Purpose: The aim of this study is to morphometrically and morphologically examine the occipital condyle, which is an important anatomical region in terms of surgery and forensic medicine, and its surrounding structures, to evaluate the change in mean values according to gender and age, and to evaluate the correlation of the measurements obtained.

Methods: 180 (90 men, 90 women) CBCT images selected from the archive of Ankara University Faculty of Dentistry. Occipital Condyle length and width, Hypoglossal Canal-Basion distance, Hypoglossal Canal-Opistion distance, Hypoglossal Canal-Occipital Condyle anterior and posterior border distance, Occipital Condyle thickness, Hypoglossal Canal length, the widest diameter of Hypoglossal Canal, the narrowest diameter of the Hypoglossal Canal, the length of the Jugular Tubercle, the width of the Jugular Tubercle, the anterior intercondylar distance, the posterior intercondylar distance, and the Foramen Magnum index were measured.

Influence of voxel size and filter type on detecting vertical root fracture using cone-beam computed tomography.

Vertical root fractures (VRFs) can start at any level of the root and progress longitudinally to the coronal attachment. This study aimed to investigate the effects of different exposure parameters used when obtaining CBCT scans in detecting simulated VRFs. Hence, 80 intact human mandibular single-rooted pre-molar teeth without root fractures were included in the study.

Development and validation of intraoral periapical radiography-based machine learning model for periodontal defect diagnosis.

Radiographic determination of the bone level is useful in the diagnosis and determination of the severity of the periodontal disease. Various two- and three-dimensional imaging modalities offer choices for imaging pathologic processes that affect the periodontium. In recent years, innovative computer techniques, especially artificial intelligence (AI), have begun to be used in many areas of dentistry and are helping increase treatment and diagnostic performance.

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model's performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists.

Automatic Feature Segmentation in Dental Periapical Radiographs.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software.

Analysing the effect of sodium bicarbonate and glycine air polishing on tooth surfaces with two different imaging methods.

The aim of this study was to compare two different imaging methods by assessing changes caused by sodium bicarbonate and glycine air polishing on the tooth surfaces. Fourteen single root teeth with exposed root surfaces were included into the study. The teeth were randomly divided into two groups: sodium bicarbonate and glycine group.

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.

Fracture Resistance of Esthetic Prefabricated and Custom-Made Crowns for Primary Molars After Artificial Aging.

The purpose of this in vitro study was to compare the fracture resistance and survival of various esthetic crowns for primary molars after artificial aging via chewing simulation. A typodont tooth (mandibular primary second molar) was prepared to receive five different types of crowns as follows (n equals 10): prefabricated fiberglass (PF); CAD/CAM zirconia (CZ); CAD/CAM resin-ceramic (CR); composite- strip (CS); and prefabricated zirconia (PZ) as control. All specimens were subjected to 750,000 cycles of thermomechanical loading to artificially simulate three years of clinical service.

A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs.

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations.

Subject And Methods: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs.

Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images.

The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models.

Investigation of the prevalence and main features of skull-base anomalies and characteristics of the sphenoid sinus using cone-beam computed tomography.

Objectives: This study aimed to define the prevalence and characteristics of skull base anomalies and the features of sphenoid sinus pneumatization (SSP).

Materials And Methods: Five hundred cone-beam computed tomography scans were evaluated retrospectively for the presence of fossa navicularis magna (FNM), canalis basilaris medianus (CBM), sphenoid emissary foramen (SEF), and/or Onodi cells (OC). Patterns of the SSP and sphenoid sinus mucosa dimensions (SSMD) were also recorded.

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).

Comparison of Intracranial and Extracranial Carotid Artery Calcifications between Obstructive Sleep Apnea Patients and Healthy Individuals: A Combined Cone-Beam Computed Tomography and Polysomnographic Study.

Purpose: This study aimed to compare the presence and grades of intra- and extracranial carotid artery calcifications between obstructive sleep apnea (OSA) and non-OSA patients.

Methods: CBCT records of 190 patients (95 OSA patients and 95 non-OSA patients) were retrospectively collected and analyzed. Patient demographic data, including age and gender for both study groups and body mass index (BMI), and apnea-hypopnea index (AHI) for OSA patients were recorded.

Technical details for inset of flaps in transfer of double-level gastroepiploic lymph node flaps for lower extremity lymphedema.

Background: In this study, the method that can be followed to ensure rapid and uncomplicated recovery of lymph node flap (LNF) applied in the medial of the ankle for lymphedema treatment was investigated.

Methods: Thirty-seven patients with class II of lower limb lymphedema underwent transfer of gastroepiploic LNF to the medial ankle and popliteal fossa areas. At the popliteal fossa region, the wound could always be closed primarily by the advancement of neighboring skin.

Influence of Cone-Beam Computed Tomography Voxel Sizes in the Detection of Chemically Induced External Root Resorptions.

BACKGROUND External root resorption usually does not present a clinical sign or symptom, and, therefore, diagnosis is mainly based on radiographic examination. Many studies confirmed the advantage and accuracy of cone-beam computed tomography (CBCT) in evaluating root resorptions. We aimed to evaluate the diagnostic accuracy of CBCT images of chemically induced external root resorptions on extracted human teeth taken in different voxel sizes.

Odontogenic fibromyxoma: A case report in myasthenia gravis patient and review of the literature.

Introduction And Importance: Odontogenic fibromyxoma is generally slow-growing, benign, asymptomatic, present with painless swelling in the jaw. Pain is mostly seen in the case of infection, adjacent anatomical structures or neural involvement. When the English-language literature is searched, only 62 cases are found about odontogenic fibromxoma which means it is really rare pathology.

Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups.

Objectives: In this single-center study, we aimed to propose a machine-learning model and assess its ability with clinical data to classify low- and high-risk thymoma on fluorine-18 (18F) fluorodeoxyglucose (FDG) (18F-FDG) PET/computed tomography (CT) images.

Methods: Twenty-seven patients (14 male, 13 female; mean age: 49.6 ± 10.