Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges.

Automatic feature segmentation in dental panoramic radiographs.

Objective: The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography.

Methods: This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists.

Dilemma with implant placement in patients with florid cemento-osseous dysplasia: A literature review.

Methodology: An electronic search was done in PUBMED, SCOPUS, and a hand search was done in radiology, periodontology, and oral surgery journals. The search yielded 428 results, from which only 6 articles were selected for this literature review. Both prospective and retrospective studies were included.

Central giant-cell granuloma in a patient with neurofibromatosis type 1: 7 years of follow-up.

Neurofibromatosis type 1 (NF1) is an autosomally dominant tumor suppressor syndrome and multisystem disease. Central giant-cell granulomas (CGCGs) can be seen in patients with NF1. A 21-year-old female was diagnosed with two CGCGs, one in the mandible and then one in the maxilla, in a 7-year period.

Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study.

Background: This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns.

Methods: A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method.

Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images.

The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment.

Detection of tooth numbering, frenulum attachment, gingival overgrowth, and gingival inflammation signs on dental photographs using convolutional neural network algorithms: a retrospective study.

Objectives: This study aimed to develop an artificial intelligence (AI) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral photographs and to evaluate the performance of this model.

Method And Materials: A total of 654 intraoral photographs were used in the study (n = 654). All photographs were reviewed by three periodontists, and all teeth, frenulum attachment, gingival overgrowth areas, and gingival inflammation signs on photographs were labeled using the segmentation method in a web-based labeling software.

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.

Nasopharynx evaluation in children of unilateral cleft palate patients and normal with cone beam computed tomography.

Objective: This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT).

Methods: A retrospective study consisted of 54 patients, of which 27 patients were unilateral CL/P, remaining 27 patients have no CL/P. Eustachian tubes orifice (ET), Rosenmuller fossa (RF) depth, presence of pharyngeal bursa (PB), the distance of posterior nasal spine (PNS)-pharynx posterior wall were quantitatively evaluated.

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.

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.

Modified Radical Mastectomy vs Breast-Conserving Surgery: Current Clinical Practice in Women with Early Stage Breast Cancer at a Corporate Tertiary Cancer Center in India.

Unlabelled: Breast cancer is the most common cancer in women in urban India and surgery has one of the definitive roles in treating this cancer. Over the decades, multiple studies have been published and they have shown that BCS followed by radiotherapy has equivalent disease-free survival (DFS) and overall survival (OS) as compared with MRM. The surgeon has the main role in explaining the treatment options to the patient.

Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm.

Objectives: Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography.

Methods: 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs.

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images.

Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training.

Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system.

Objectives: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography.

Methods: One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskişehir, Turkey and based on a deep CNN method, was proposed to be evaluated.

Osteopathia striata in the mandible with cranial sclerosis: a case report and review of the literature.

Osteopathia striata with cranial sclerosis (OS-CS) is a bone dysplasia characterized by a linear striated pattern of sclerosis, especially in the long bones, and cranial sclerosis. It has variable clinical findings but distinctive radiological findings. Multiple oral and dental findings have been associated with this disease and can be seen during dental and/or medical imaging of the head and neck.

Oral manifestation of Waardenburg syndrome: a case report and review of the literature.

Waardenburg syndrome is a rare autosomal dominant genetic disorder of neural crest cell migration. It is characterized by congenital sensorineural hearing loss, heterochromia iridis, depigmentation of hair and skin, and increased intercanthal distance. It is subdivided into four subtypes with I and II being most common.

Retropharyngeal internal carotid artery: a review of three cases.

Introduction: The internal carotid artery (ICA) can take multiple pathways as it extends from the carotid bifurcation to the skull base. An aberration of its normal pathway may place the ICA in a retropharyngeal position in close proximity to the posterior pharyngeal wall. Radiographic classification is based on its proximity to the pharynx and/or pathway.

Trichodentoosseous syndrome: a case report and review of literature.

Trichodentoosseous (TDO) syndrome is a rare autosomal dominant condition characterized by various dental and non-dental findings such as taurodontism, amelogenesis imperfecta, osseous dysplasia, mandibular prognathism, curly hair, dysplastic nails, which may be symptomatic or asymptomatic. TDO syndrome is divided into three subtypes that helps to categorize different features seen in patients. There are very few cases reported in the literature of TDO syndrome.

Gorham-Stout disease of the mandible: case report and review of literature of a rare type of osteolysis.

Gorham-Stout disease (GSD) is a rare form of osteolysis, the aetiology and pathogenesis of which remains controversial to this date. Although more than 200 cases of GSD have been reported so far, this disease continues to go undiagnosed in the initial stages owing to its varied clinical presentations and rarity. Through this case report of GSD in a 3-year-old boy, we discuss the slow progression of the disease over a period of 13 years.

Condylar jugular diverticulum: A report of 3 cases.

Jugular bulb diverticulum is an irregular extension of the jugular bulb into the temporal bone that may be symptomatic or asymptomatic. The jugular bulb has rarely been reported to extend into the occipital condyle; such extension is termed a condylar jugular diverticulum and is characterized as a defect in the occipital condyle contiguous with the jugular bulb. This report details 3 cases of condylar jugular diverticulum.