Patients with lumbar spinal stenosis (LSS) may experience neuropathic symptoms, such as back pain, radiating pain, and neurogenic claudication. Although the long-term outcomes of both nonsurgical and surgical treatments are similar, surgery may provide shortterm benefits, including improved symptoms and lower risk of falling. Decompression is mainly used for surgical treatment, and depending on the decompression degree and associated instability, combination therapy may be given. Minimally invasive surgery has been demonstrated to produce excellent results in the treatment of LSS. Thus, an approach aimed at understanding the overall pathophysiology and treatment methods of LSS is expected to have a better therapeutic effect.
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.
Purpose This study aimed to compare the therapeutic effects of corticosteroid irrigations and normal saline irrigations in the early inflammatory state of the salivary gland. Materials and Methods Adult male Wistar rats were divided into experimental (n=6) and control (n=3) groups. Inflammation was induced in the experimental subjects on both sides of the submandibular gland with ligation. After 14 days, both sides of the glands were de-ligated and retroductal irrigation using saline (n=3) and a corticosteroid (n=3) was performed on the left sides only. The controls (n=3) were used to normalize the gland state for the effects of diet and aging. Magnetic resonance imaging was performed to confirm inflammation and post-irrigation gland recovery by measuring relative signal intensity (SI). The glands were excised for histological examination. Results All experimental animals showed inflamed glands with increased SI and subsequent recovery of the gland with decreased SI to varying degrees. The SI of the controls showed no significant changes during the overall period. The mean SI change of the irrigated gland was higher than that of the non-irrigated side, without a significant difference. The corticosteroid-irrigated glands showed a greater change in SI than that of the saline-irrigated glands. Histology revealed that inflammation was not observed in most of the irrigated glands, while mild to moderate quantities inflammatory cells were found in non-irrigated glands. Conclusion Corticosteroid irrigation mitigated the early stages of salivary gland inflammation more effectively than normal saline.
Objectives: Lingual mandibular bone depression (LMBD) is a developmental bony defect in the lingual aspect of the mandible that does not require any surgical treatment. It is sometimes confused with a cyst or another radiolucent pathologic lesion on panoramic radiography. Thus, it is important to differentiate LMBD from true pathological radiolucent lesions requiring treatment. This study aimed to develop a deep learning model for the fully automatic differential diagnosis of LMBD from true pathological radiolucent cysts or tumors on panoramic radiographs without a manual process and evaluate the model’s performance using a test dataset that reflected real clinical practice. Methods: A deep learning model using the EfficientDet algorithm was developed with training and validation data sets (443 images) consisting of 83 LMBD patients and 360 patients with true pathological radiolucent lesions. The test data set (1500 images) consisted of 8 LMBD patients, 53 patients with pathological radiolucent lesions, and 1439 healthy patients based on the clinical prevalence of these conditions in order to simulate real-world conditions, and the model was evaluated in terms of accuracy, sensitivity, and specificity using this test data set. Results: The model’s accuracy, sensitivity, and specificity were more than 99.8%, and only 10 out of 1500 test images were erroneously predicted. Conclusion: Excellent performance was found for the proposed model, in which the number of patients in each group was composed to reflect the prevalence in real-world clinical practice. The model can help dental clinicians make accurate diagnoses and avoid unnecessary examinations in real clinical settings.
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