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Objectives: The purpose of this study is to investigate the morphological changes that occur when mesiodens is located within the nasopalatine canal, as well as clinical characteristics., Methods: Clinical records and CT images of patients who had mesiodens in the nasopalatine canal were retrospectively analysed. In addition to demographic information, clinical symptoms and complications associated with extraction of mesiodens were recorded. Using CT images, number, location, size, and tooth morphology were evaluated., Results: This study included 32 patients and 38 mesiodens within the nasopalatine canal. Supernumerary teeth exhibited a characteristic feature of thin and elongated shape in the canal (narrow width and elongation were observed in 96.6% and 53.3% of the patients, respectively). Fusion was found in 4 patients and dilaceration in 12. A complication occurred in 2 patients, which was tooth remnant, not a neurologic complication. Only 5 mesiodens could be detected in the nasopalatine canal on panoramic images., Conclusions: Morphological abnormalities in mesiodens within the nasopalatine canal were frequently detected, and these could be effectively diagnosed through 3D imaging analysis., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology.)

Objectives: This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel., Methods: PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR)., Results: The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%., Conclusions: This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: The purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation for detection and classification of the multiple diseases., Methods: We developed a deep CNN modified from YOLOv3 for detecting and classifying odontogenic cysts and tumors of both jaws. Our data set of 1282 panoramic radiographs comprised 350 dentigerous cysts (DCs), 302 periapical cysts (PCs), 300 odontogenic keratocysts (OKCs), 230 ameloblastomas (ABs), and 100 normal jaws with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. We evaluated the classification performance of the developed CNN by calculating sensitivity, specificity, accuracy, and area under the curve (AUC) for diseases of both jaws., Results: The overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity,91.3% accuracy, and 0.86 AUC using the CNN with unaugmented data set to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the CNN with augmented data set. CNN using augmented data set had the following sensitivities, specificities, accuracies, and AUCs: 91.4%, 99.2%, 97.8%, and 0.96 for DCs, 82.8%, 99.2%, 96.2%, and 0.92 for PCs, 98.4%,92.3%,94.0%, and 0.97 for OKCs, 71.7%, 100%, 94.3%, and 0.86 for ABs, and 100.0%, 95.1%, 96.0%, and 0.97 for normal jaws, respectively., Conclusion: The CNN method we developed for automatically diagnosing odontogenic cysts and tumors of both jaws on panoramic radiographs using data augmentation showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.

Accidental overextension of filling material during endodontic treatment may cause mechanical and chemical irritation of the adjacent tissue. In this case series, seven patients who complained of neurologic complication after overextension of canal filling material during endodontic treatment were presented. Overextensions of filling material into the surrounding tissues, such as the mandibular canal, bone marrow, and submucosal layer, were confirmed with panoramic radiograph and computed tomographic images. When patients complain of neurological disturbances after root canal treatment, proper radiographic images should be taken to verify any overextension of materials and to determine the specific location of the overfilled materials.

Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis., Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers., Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD., Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

Haemophilic pseudotumour is a rare disease that occurs most often in femur, tibia, ilium or pelvic bone of a patient with haemophilia. Thus far, there have been only 31 reported cases in jaw bones and paranasal sinuses. Among them, the mandible is a more common site than the maxilla or paranasal sinuses. Here, we report a case of haemophilic pseudotumour in two parts of the maxilla. Contrast-enhanced CT showed an expansive and thinly corticated lesion with fluid attenuation at the left anterior maxilla which seemed like a post-operative maxillary cyst, ameloblastoma or odontogenic cyst. In addition, the thickened left palatal process of the maxilla seemed like fibrous dysplasia or intraosseous vascular malformation. Since haemophilic pseudotumour is not pathognomonic in radiological findings, when a patient who suffered from haemophilia or had taken anticoagulating agents has jaw lesion, haemophilic pseudotumour should be included in a differential diagnosis.

Intraosseous vascular malformation (IVM) is a relatively rare pathological condition that may pose significant risks, such as excessive bleeding, during surgical procedures. We present a case of an 18-year-old female patient with firm swelling in the left maxilla. A bony expansion over the left half of the maxilla with preservation of the outer cortex and a ground glass appearance on CT images initially revealed a possibility of fibrous dysplasia. However, a tentative diagnosis of IVM was made based on the vascular nature of the lesion as well as the patient's surgical history and additional imaging findings. IVM should be included in the differential diagnosis of an expansile bony lesion with trabecular alteration. Through the literature review, it was found that imaging findings, such as a neurovascular canal widening on CT images and a hyperintense signal on T1 weighted MR images, might be helpful in differentiating IVM from other pathologies.

Osteoblastoma is a benign neoplasm which commonly occurs in the vertebral column and long bones. The tumour grows slowly and rarely recurs after surgery. This report presents the clinicopathological and radiological findings of a case of recurrent osteoblastoma in the maxilla. A 7-year-old male patient visited our department with chief complaints of left facial swelling and pain. A panoramic radiograph showed a homogeneous radio-opaque expansile lesion in the left maxilla. The lesion was thought to be fibrous dysplasia and the patient underwent a surgical excision using the Caldwell-Luc procedure. Histopathological examination of the lesion confirmed it as benign osteoblastoma. The lesion recurred 6 months after the initial surgery. CT images revealed a large mass with multiple internal calcifications. Subsequently, the patient underwent mass excision with subtotal left maxillectomy. Follow-up CT scans at 1 year intervals showed no recurrence for 5 years.

A dentinogenic ghost cell tumour (DGCT) is an extremely rare odontogenic tumour which is considered as a solid, neoplastic variant of calcifying odontogenic cyst. Intraosseous DGCTs are more aggressive than extraosseous DGCTs and have a high propensity for local recurrence. This report describes a case of a diagnosis of recurrent DGCT at the primary site and a distant donor site. A 25-year-old female patient visited a dental hospital for a complaint of facial swelling for the previous month. Incisional biopsy was performed and the specimen was diagnosed as DGCT. Partial mandibulectomy for tumour resection and iliac bone graft was performed. 2 years later, the tumour recurred on the mandible and iliac bone. The recurrent lesion on the donor site was diagnosed as metastasized DGCT. This report highlights the possibility of distant metastasis occurring at a graft donor site.

Objectives: This study aimed to show that the horizontal relationship between the mandibular canal and the alveolar crest can influence the available bone height (ABH) measurement on panoramic radiographs., Methods: 92 mandibular edentulous sites of panoramic computed radiographs and reformatted CT images of 77 patients were used. Selected CT images were categorized into four types according to the relative location of the peak of the alveolar crest to the mandibular canal. One oral and maxillofacial radiologist measured the ABH twice on both imaging modalities with an interval of 7 days and compared the measurement differences according to the type., Results: The absolute average value of the differences in measurement between the values of ABHs on panoramic images and CT images was 0.97 mm. Significant difference was found only between the mean values of ABHs for Type 1 (0.60 mm), where the alveolar crest is located in the buccal side or central area with respect to the mandibular canal, and Type 4 (1.46 mm), where the alveolar crest is in the lingual side to the mandibular canal (p < 0.05)., Conclusions: The relative horizontal location of the alveolar crest with respect to the mandibular canal affected the ABH measurement on panoramic radiographs. In particular, ABH is overestimated when there has been resorption of the buccal aspect of the ridge, moving the alveolar crest lingually.