Mandibular fracture is one of the most frequent injuries in oral and maxillo-facial surgery. Radiologists diagnose mandibular fractures using panoramic radiography and cone-beam computed tomography (CBCT). Panoramic radiography is a conventional imaging modality, which is less complicated than CBCT. This paper proposes the diagnosis method of mandibular fractures in a panoramic radiograph based on a deep learning system without the intervention of radiologists. The deep learning system used has a one-stage detection called you only look once (YOLO). To improve detection accuracy, panoramic radiographs as input images are augmented using gamma modulation, multi-bounding boxes, single-scale luminance adaptation transform, and multi-scale luminance adaptation transform methods. Our results showed better detection performance than the conventional method using YOLO-based deep learning. Hence, it will be helpful for radiologists to double-check the diagnosis of mandibular fractures.

A 16-year-old male presented with pain in the right posterior mandible on chewing that had lasted for several months. The radiographic features of the lesion included a radiolucent-radiopaque mixed-density mass with a radiolucent rim attached to the root of the mandibular right first molar. The preliminary radiographic diagnosis was benign cementoblastoma, which was confirmed by histopathological examination following surgical excision. The lesion recurred 3 years after treatment; radiographically, it consisted of 3 round foci with mixed radiopacity, each with a radiolucent rim near the root of the mandibular right second premolar and the edentulous postoperative region. The lesion was diagnosed as recurrent benign cementoblastoma and a second surgery was scheduled. This report presented an unusual case of recurrent benign cementoblastoma following surgical excision and extraction of the involved tooth, along with a literature review on reported cases of recurrent benign cementoblastoma with a focus on its clinical features and the best treatment options.

Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses.

This report presents the case of a 49-year-old man who presented with giant masses that had recently grown on the bilateral sides of the tongue. A clinical examination revealed rubbery yellowish lesions protruding from the tongue. A panoramic radiograph showed an enlarged soft tissue shadow of the tongue. Computed tomography showed well-defined circumscribed mass exhibiting a homogeneous low density on the bilateral sides of the tongue. On magnetic resonance images, the masses showed a high signal intensity on T1-weighted images and iso-signal intensity with partially hyperintense margin on fat-suppressed T2-weighted images. Surgical excision was performed, and a histopathologic examination confirmed the diagnosis of lipoma. The patient recovered well with no sign of recurrence. A giant lipoma is defined as a lipoma larger than 5 cm in diameter. A literature review of giant lipomas of the tongue is also presented herein.