Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Objective: This study aimed to identify factors associated with the recovery of mandibular nerve disturbance and to predict the possibility of recovery tailored to individual patients. Materials and Methods: Patients who visited the dental hospital with symptoms of mandibular nerve disturbance from April 2015 to September 2020 were studied. Patients were divided into two groups based on treatment outcomes: recovered or non-recovered. Variables related to recovery included age, sex, onset event of the nerve disturbance, affected area, imaging findings, and treatment methods. The correlation between recovery and these variables was analyzed using the Chi-square test and Fisher’s exact test. Results: A total of 328 patients were included in the study.Among the variables associated with recovery, the onset event of the symptom (P-value=0.02) and imaging findings (P-value=0.04) were statistically significant. Among the significant variables, the highest proportion of patients (77.78%) recovered without symptoms of onset event, while implant surgery showed the lowest recovery rate (34.25%). Regarding imaging findings, the recovery rate was highest in cases of suspected canal damage (58.82%), while no patients recovered from compression of the canal (0.00%). Conclusion This study highlights the importance of large-scale data analysis and a thorough evaluation of clinical variables to understand mandibular nerve disturbances. The findings provide a basis for improving treatment strategies and reducing the impact of nerve disturbances on patients’ quality of life.

Posterior reversible encephalopathy syndrome (PRES) is a disorder of reversible vasogenic edema which mainly involves the parieto-occipital lobes in various clinical settings. Isolated involvement of the brainstem, basal ganglia, and cerebellum is rare. An 81-year-old female was admitted because of a decreased level of consciousness, and she had a head trauma history a day before. Brain magnetic resonance imaging (MRI) showed extensive confluent T2 hyperintensity with swelling involving the bilateral thalami, brainstem, and cerebellar peduncle without cortical lesions. We reports a case of central-variant PRES after traumatic brain injury.

Posterior reversible encephalopathy syndrome (PRES) is a disorder of reversible vasogenic edema which mainly involves the parieto-occipital lobes in various clinical settings. Isolated involvement of the brainstem, basal ganglia, and cerebellum is rare. An 81-year-old female was admitted because of a decreased level of consciousness, and she had a head trauma history a day before. Brain magnetic resonance imaging (MRI) showed extensive confluent T2 hyperintensity with swelling involving the bilateral thalami, brainstem, and cerebellar peduncle without cortical lesions. We reports a case of central-variant PRES after traumatic brain injury.

Posterior reversible encephalopathy syndrome (PRES) is a disorder of reversible vasogenic edema which mainly involves the parieto-occipital lobes in various clinical settings. Isolated involvement of the brainstem, basal ganglia, and cerebellum is rare. An 81-year-old female was admitted because of a decreased level of consciousness, and she had a head trauma history a day before. Brain magnetic resonance imaging (MRI) showed extensive confluent T2 hyperintensity with swelling involving the bilateral thalami, brainstem, and cerebellar peduncle without cortical lesions. We reports a case of central-variant PRES after traumatic brain injury.

Purpose: This study aimed to propose a methodological approach for reducing the radiation dose in pediatric conebeam computed tomography (CBCT), focusing exclusively on balancing image quality with dose optimization. Materials and Methods: The dose-area product (DAP) for exposure was reduced using copper-plate attenuation of an X-ray source. The thickness of copper (Cu) was increased from 0 to 2.2 mm, and 10 different DAP levels were used. The QUART DVT_AP phantom and pediatric radiologic dentiform were scanned under the respective DAP levels. The contrast-to-noise ratio (CNR), image homogeneity, and modulation transfer function (MTF) were analyzed using the QUART DVT_AP phantom. An expert evaluation (overall image grade, appropriateness of field of view, artifacts, noise, and resolution) was conducted using pediatric dentiform images. The critical DAP level was determined based on phantom and dentiform analysis results. Results: CNR and image homogeneity decreased as the DAP was reduced; however, there was an inflection point of image homogeneity at Cu 1.6 mm (DAP = 138.00 mGy·cm2 ), where the value started increasing. The MTF showed constant values as the DAP decreased. The expert evaluation of overall image grades showed “no diagnostic value” for dentiform images with Cu 1.9-2.2 mm (DAP = 78.00-103.33 mGy·cm2 ). The images with Cu 0-1.6 mm (DAP = 138.00-1697.67 mGy·cm2 ) had a “good,” “moderate,” or “poor but interpretable” grade. Conclusion Reducing DAP beyond a 1.6-mm Cu thickness degraded CBCT image quality. Image homogeneity and clinical image grades indicated crucial decision points for DAP reduction in pediatric CBCT scans.

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen’s ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.