Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Purpose: The objective of this study was to propose a method for developing a clinical phantom to reproduce various diseases that are clinically prevalent in the field of dentistry. This could facilitate diverse clinical research without unnecessarily exposing patients to radiation.Material and MethodsThis study utilized a single dry skull, which was visually and radiographically examined to evaluate its condition. Existing lesions on the dry skull were preserved, and other relevant lesions were artificially created as necessary. These lesions were then documented using intraoral radiography and cone-beam computed tomography. Once all pre-existing and reproduced lesions were confirmed by the consensus of 2 oral and maxillofacial radiologists, the skull was embedded in a soft tissue substitute. To validate the process, cone-beam computed tomography scans and panoramic radiographs were obtained of the fabricated phantom. All acquired images were subsequently evaluated. Results: Most lesions could be identified on panoramic radiographs, although some sialoliths and cracked teeth were confirmed only through cone-beam computed tomographic images. A small gap was observed between the epoxy resin and the bone structures. However, 2 oral and maxillofacial radiologists agreed that this space did not meaningfully impact the interpretation process. Conclusion The newly developed phantom has potential for use as a standardized phantom within the dental field. It may be utilized for a variety of imaging studies, not only for optimization purposes, but also for addressing other experimental issues related to both 2- and 3-dimensional diagnostic radiography.

Long-term administration of levodopa (L-DOPA) to patients with Parkinson’s disease (PD) commonly results in involuntary dyskinetic movements, as is known for L-DOPA-induced dyskinesia (LID). 5-Hydroxytryptophan (5-HTP) has recently been shown to alleviate LID; however, no biochemical alterations to aberrant excitatory conditions have been revealed yet. In the present study, we aimed to confirm its anti-dyskinetic effect and to discover the unknown molecular mechanisms of action of 5-HTP in LID. We made an LID-induced mouse model through chronic L-DOPA treatment to 6-hydroxydopamine-induced hemi-parkinsonian mice and then administered 5-HTP 60 mg/kg for 15 days orally to LID-induced mice. In addition, we performed behavioral tests and analyzed the histological alterations in the lesioned part of the striatum (ST). Our results showed that 5-HTP significantly suppressed all types of dyskinetic movements (axial, limb, orolingual and locomotive) and its effects were similar to those of amantadine, the only approved drug by Food and Drug Administration. Moreover, 5-HTP did not affect the efficacy of L-DOPA on PD motor mani-festations. From a molecular perspective, 5-HTP treatment significantly decreased phosphorylated CREB and ΔFosB expression, commonly known as downstream factors, increased in LID conditions. Furthermore, we found that the effects of 5-HTP were not mediated by dopamine1 receptor (D1)/DARPP32/ERK signaling, but regulated by AKT/mTOR/S6K signaling, which showed different mechanisms with amantadine in the denervated ST. Taken together, 5-HTP alleviates LID by regulating the hyperactivated striatal AKT/mTOR/S6K and CREB/ΔFosB signaling.

Nodular fasciitis (NF) is a benign myofibroblastic proliferation that grows very rapidly, mimicking a sarcoma on imaging. It is treated by local excision, and recurrence has been reported in only a few cases, even when excised incompletely. The most prevalent diagnoses of temporomandibular joint (TMJ) masses include synovial chondromatosis, pigmented villonodular synovitis, and sarcomas. Cases of NF in the TMJ are extremely rare, and only 3 cases have been reported to date. Due to its destructive features and rarity, NF has often been misdiagnosed as a more aggressive lesion, which could expose patients to unnecessary and invasive treatment approaches beyond repair. This report presents a case of NF in the TMJ, focusing on various imaging features, along with a literature review aiming to determine the hallmark features of NF in the TMJ and highlight the diagnostic challenges.

Fibrodysplasia ossificans progressiva is a rare hereditary disorder characterized by progressive heterotopic ossifica-tion in muscle and connective tissue, with few reported cases affecting the head and neck region. Although plain radiographic findings and computed tomography features have been well documented, limited reports exist onmagnetic resonance findings. This report presents 2 cases of fibrodysplasia ossificans progressiva, one with limited mouth opening due to heterotopic ossification of the lateral pterygoid muscle and the other with restricted neck movement due to heterotopic ossification of the platysma muscle. Clinical findings of restricted mouth opening or limited neck movement, along with radiological findings of associated heterotopic ossification, should prompt consideration of fibrodysplasia ossificans progressiva in the differential diagnosis. Dentists should be particularly vigilant with patients diagnosed with fibrodysplasia ossificans progressiva to avoid exposure to diagnostic biopsy andinvasive dental procedures.

Mucormycosis is a rare, invasive fungal infection that progresses aggressively and requires prompt surgery and appropriate treatment. The number of cases of mucormycosis in coronavirus disease 2019 (COVID-19) patients has recently increased, and patients with uncontrolled diabetes mellitus are particularly at an elevated risk of infection. This report presents a case of mucormycosis-related osteomyelitis of the maxilla in a 37-year-old man with diabetes mellitus. The patient complained of severe and persistent pain in the right maxilla, accompanied by increased tooth mobility and headache. On contrast-enhanced computed tomographic images, gas-forming osteomyelitis of the right maxilla was observed. Destruction of the maxilla and palatine bone then proceeded aggressively. Sequestrectomy was performed on the right maxilla, and the histopathological diagnosis was mucormycosis. Further investigation after the first operation revealed the patient's history of COVID-19 infection.

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

Purpose: This study was conducted to measure the radiation exposure and image quality of various cone-beam computed tomography (CBCT) machines under common clinical conditions and to analyze the correlation between them. Materials and Methods: Seven CBCT machines used frequently in clinical practice were selected. Because each machine has various sizes of fields of view (FOVs), 1 large FOV and 1 small FOV were selected for each machine. Radiation exposure was measured using a dose-area product (DAP) meter. The quality of the CBCT images was analyzed using 8 image quality parameters obtained using a dental volume tomography phantom. For statistical analysis, regression analysis using a generalized linear model was used. Results: Polymethyl-methacrylate (PMMA) noise and modulation transfer function (MTF) 10% showed statistically significant correlations with DAP values, presenting positive and negative correlations, respectively (P<0.05). Image quality parameters other than PMMA noise and MTF 10% did not demonstrate statistically significant correlationswith DAP values. Conclusion As radiation exposure and image quality are not proportionally related in clinically used equipment, it is necessary to evaluate and monitor radiation exposure and image quality separately.

PURPOSE: . The purpose of this study was to investigate the effect of barium silicate filler contents on mechanical properties of resin nanoceramics (RNCs) for additive manufacturing (AM). MATERIALS AND METHODS: . Additively manufactured RNC specimens were divided into 4 groups depending on the content of ceramic fillers and polymers: 0% barium silicate and 100% polymer (B0/P10, control group); 50% barium silicate and 50% polymer (B5/P5); 60% barium silicate and 40% polymer (B6/P4); 67% barium silicate and 33% polymer (B6.7/P3.3). The compressive strength (n = 15) and fracture toughness (n = 12) of the specimens were measured, and scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) analyses were performed. Independent sample Kruskal-Wallis tests were performed on the compressive strength and fracture toughness test results, and the significance of each group was analyzed at the 95% confidence interval through post-tests using the Bonferroni’s method. RESULTS: . B6/P4 and B6.7/P3.3 exhibited much higher yield strength than B0/P10 and B5/P5 (P < .05). Compared to the control group (B0/P10), the other three groups exhibited higher ultimate strength (P < .05). The fracture toughness of B6/P4 and B6.7/P3.3 were similar (P > .05). The content of barium silicate and fracture toughness showed a positive correlation coefficient (R = 0.582). SEM and EDS analyses revealed the presence of an oval-shaped ceramic aggregate in B6/P4 specimens, whereas the ceramic filler and polymer substrate were homogeneously mixed in B6.7/P3.3. CONCLUSION . Increasing the ceramic filler content improves the mechanical properties, but it can be accompanied by a decrease in the flowability and the homogeneity of the slurry.

Purpose: This study aimed to investigate the panoramic imaging features of cleidocranial dysplasia (CCD) with a relatively large sample. Materials and Methods: The panoramic radiographs of 40 CCD patients who visited Seoul National University Dental Hospital between 2004 and 2018 were analyzed. Imaging features were recorded based on the consensus of 2 radiologists according to the following criteria: the number of supernumerary teeth and impacted teeth; the shape of the ascending ramus, condyle, coronoid process, sigmoid notch, antegonial notch, and hard palate; the mandibular midline suture; and the gonial angle. Results: The mean number of supernumerary teeth and impacted teeth were 6.1 and 8.3, respectively, and the supernumerary teeth and impacted teeth were concentrated in the anterior and premolar regions. Ramus parallelism was dominant (32 patients, 80.0%) and 5 patients (12.5%) showed a mandibular midline suture. The majority of mandibular condyles showed a rounded shape (61.2%), and most coronoid processes were triangular (43.8%) or round (37.5%). The mean gonial angle measured on panoramic radiographs was 122.6°. Conclusion Panoramic radiographs were valuable for identifying the features of CCD and confirming the diagnosis. The presence of numerous supernumerary teeth and impacted teeth, especially in the anterior and premolar regions, and the characteristic shapes of the ramus, condyle, and coronoid process on panoramic radiographs may help to diagnose CCD.