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. Clinical studies with information on the pre-implant condition of the site, detailed implant procedure, and follow-up after implant placement of more than 6 months were only considered for this review. RESULTS AND CONCLUSION: Limited clinical studies, shorter follow-up periods were the shortcomings of this review. However, it can be summarized that dental implants should not be placed at the site of FCOD, however can be placed at adjacent sites. Variations in implant type or the implant length had no bearing on the survival of implants at the sites of FCOD.

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. Increased incidence of CGCGs in NF1 patients was thought to be caused by an underlying susceptibility to developing CGCG-like lesions in qualitatively abnormal bone, such as fibrous dysplasia. However, germline and somatic truncating second-hit mutations in the NF1 gene have been detected in NF1 patients with CGCGs, validating that they are NF1-associated lesions. Oral manifestations in patients with NF1 are very common. Knowledge of these manifestations and the genetic link between NF1 and CGCGs will enhance early detection and enable optimal patient care.

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. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS: The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS: AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.

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. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.

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. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. RESULTS: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. CONCLUSION: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique.

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. In addition, tooth numbering was carried out according to the FDI system. An AI model was developed with the help of YOLOv5x architecture with labels of 16,795 teeth, 2,493 frenulum attachments, 1,211 gingival overgrowth areas, and 2,956 gingival inflammation signs. The confusion matrix system and ROC (receiver operator characteristic) analysis were used to statistically evaluate the success of the developed model. RESULTS: The sensitivity, precision, F1 score, and AUC (area under the curve) for tooth numbering were 0.990, 0.784, 0.875, and 0.989; for frenulum attachment these were 0.894, 0.775, 0.830, and 0.827; for gingival overgrowth area these were 0.757, 0.675, 0.714, and 0.774; and for gingival inflammation sign 0.737, 0.823, 0.777, and 0.802, respectively. CONCLUSION: The results of the present study show that AI systems can be successfully used to interpret intraoral photographs. These systems have the potential to accelerate the digital transformation in the clinical and academic functioning of dentistry with the automatic determination of anatomical structures and dental conditions from intraoral photographs.

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. RESULTS: The main effect of the CL/P groups was found to be effective on RF depth-right (p < 0.001) and RF depth-left (p < 0.001). The interaction effect of gender and CL/P groups was not influential on measurements. The cleft-side main effect was found to be effective on RF depth-left (p < 0.001) and RF depth-right (p = 0002). There was no statistically significant relationship between CL/P groups and the presence of bursa pharyngea. CONCLUSIONS: Because it is the most common site of nasopharyngeal carcinoma (NPC), the anatomy of the nasopharynx should be well known in the early diagnosis of NPC.

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. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.

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. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. RESULTS: Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. CONCLUSIONS: CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.

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. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

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. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements. RESULTS: Fifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively. CONCLUSION: Deep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts.

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.

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. It is a prospective study conducted at Vedant Cancer and Multispeciality Hospital in a metropolitan city, Thane, India. Patients with stage I or II breast cancer with tumor size less than 5 cm were included in the study. Patients with locally advanced and metastatic breast cancer were excluded from the study. The study population was early breast cancer patients registered and waiting for surgery (n = 86) at Vedant Cancer and Multispeciality Hospital from November 2019 to end of April 2020. The total number of females enrolled in the study were 86 and out of this, 79.1% (n = 68) females opted for MRM and 20.9% (n = 18) females opted for BCS in which 8 patients had changed their decision after re-counseling in the ward from MRM to BCS. The most common reasons selected by patients to undergo MRM were fear of cancer recurrence (30.2%, n = 26), avoidance of side effects of radiation therapy (25.5%, n = 22) and fear of radiation therapy (23.2%, n = 20). Surgeon had decided the surgical option in 79.1% (n = 68) cases. The study shows that the treating surgeon and patient's husband are the principal persons who decide the surgical option and active participation of women during counseling is an important factor. Supplementary Information: The online version contains supplementary material available at 10.1007/s13193-021-01457-8.

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. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. RESULTS: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. CONCLUSION: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

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 Eskisehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores. RESULTS: When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively. CONCLUSION: The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period.

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. Dentists and clinicians must be familiar with these signs to differentiate them from pathosis or erroneous radiographs. In the following case, we present a patient with OS-CS that presented at The University of Florida College of Dentistry with multiple craniofacial manifestations of this syndrome that were seen on a panoramic radiograph, which is one of the most commonly requested radiographs by dentists.

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. These subtypes are categorized based on genetic mutations. Although medical literature has well documented this syndrome, dental and radiographical findings have been rarely presented. In this case report and literature review, we have presented and discussed oral as well as head and neck radiology findings of a 20-year-old girl with Waardenburg syndrome.

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. We present a series of three cases of retropharyngeal ICAs, our goal is to report and classify these variations. CASE PRESENTATION: CASE 1: Retropharyngeal right ICA. Minimum distance to the pharyngeal wall was ~ 4.9 mm (high risk of vascular injury) with a tortuous pathway. CASE 2: Bilateral retropharyngeal ICA. ICAs were in contact with the posterior pharyngeal wall (very high risk of vascular injury). The left has a kinking pathway, the right tortuous. CASE 3: Bilateral retropharyngeal ICA. Minimum distances of the right and left ICAs to the posterior pharyngeal wall were ~ 3.5 mm and ~ 3.3 mm, respectively (high risk of vascular injury). The right has a kinking pathway, the left tortuous. DISCUSSION: Closeness of the vessel to the retropharyngeal wall increases the risk of surgical and non-surgical complications. Noteworthy is that the position of the artery is not constant and can change in position over periods of time. CONCLUSION: Knowledge of the anatomy and variations of the ICA is important for oral and maxillofacial radiologists and surgeons to enable clinicians to take necessary precautions to decrease complications if performing any procedure in the region.

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. The literature review is also done with an emphasis on the role of an oral and maxillofacial radiologist in understanding the disease at its incipient stage.

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. Extension of the jugular bulb into the ipsilateral occipital condyle was noted as an incidental finding on cone-beam computed tomographic (CBCT) images of 3 patients. All 3 patients were asymptomatic, and this finding was unrelated to the initial area of interest. CBCT use is becoming ubiquitous in dentistry, as it allows 3-dimensional evaluation, unlike conventional radiography. Proper interpretation of the entire CBCT is essential, and recognition of the indicators of condylar jugular diverticulum may prevent misdiagnosis of this rare entity.