• Journal of radiological science and technology
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• v.43 no.5
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• pp.397-404
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• 2020

Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• Journal of radiological science and technology
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• v.37 no.3
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• pp.211-221
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• 2014

The clinical practices provide the students with a good opportunity to study the practical experiences in their field through the clinical training education in hospital. Now, in this study, comparing the perceptions of the clinic teachers with those of students at the clinical site. The study was conducted to the students attending universities located in Seoul and who finished the clinical practices in 2013. The questionnaires were distributed to the student and collected from them. The study were conducted to 275 questionnaires with frequency analysis, crosstabs, chi-square test and McNemar test. The major motivation was of the select radiography course was high employment rate(44.0%) and the satisfaction of radiography course was general(53.1%). 51.3% of the study answered 8 weeks current duration of clinical practices is proper. The 3-year course students answered that the period of clinical practices would be proper if it is performed in the winter vacation in their second year in college(47.3%). The 4-year course students answered that the first semester in their third years is proper for clinical practices( 27.7%). The students answered that they felt the lack in their knowledge on the professional field(32.4%) during the clinical practices and some of the practical training is different from the education performed at school(68.4%). Most of answered that they were satisfied with the clinical practices and among them they recognized the importance of the clinical practices ($3.94{\pm}0.89$). After the clinical practices, their desire for getting job as a radiography has changed from 84.1% to 82.9%. The reason why they want the job related to the radiation is because the job is stable (changed from 49.0% to 46.0% after the clinical practice) while the reason why they don't want be a radigrapher because that job is not proper for them (changed from 37.0% to 40.7% after the clinical practice) The effort should be made to enhance the position of radiation professionals through the improved education system to the students, rather giving them education for just employment.

• Journal of radiological science and technology
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• v.31 no.2
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• pp.177-182
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• 2008

Digital imaging for general rediography has many advantages over the film/screen systems, including a wider dynamic range and the ability to manipulate the images produced. The wider range means that acceptable images may by acquired at a range of dose levels, and therefore repeat exposures can be reduced. Digital imaging can result in the over use of radiation, however, because there is a tendency can be reduced. Digital imaging can result in the over use of radiation, however, because there is a tendency for images to be acquired at too high a dose. We investigated the actual exposure dose conditions on general radiography and a questionnaire survey was conducted with radiotechnologiest at medical institutions using digital radiology system. As a results, the dose of exposure was not controlled with patient's figure and dose optimization but was controlled by worker's convenience and image quality. Radio-technologiests often set up the exposure dose regardless of patient figure and body part to be examined. Many organizations, such as the International Commission on Radiological Protection, recommend to keep the dose as low as possible. In addition, they strongly recommend to keep the optimal but minimal dosage by proper training programs and constant quality control, including frequent patient dose evaluations and education.

• Physical Therapy Korea
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• v.7 no.3
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• pp.1-18
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• 2000

The present study was performed to investigate the prevalence rate of idiopathic scoliosis and to determine the effect of exercise training on scoliotic angle in elementary school children. In this study, two out of five elementary schools in Seosan city were chosen by random sampling. Seven hundred sixty four students (from four grade to the sixth grade student) were selected in two schools. Screening tests were conducted to find idiopathic scoliosis. Among the 764 individuals, 139 subjects who showed positive sign in physical examination took whole spine radiography. Thirty six subjects who had a curve of 10 or greater and consented to participate in the exercise program were selected for the exercise program. The exercise program was performed four times a week for 5 months. The results of this study were as follows: 1) One hundred thirty nine subjects showed positive sign in the scoliosis screening test. 2) The overall prevalence of curve of $10^{\circ}or$ greater in X-ray finding was 8.15%. The prevalencies of curve of $10^{\circ}or$ greater in male and female were 7.1% and 9.2%, respectively. 3) Scoliosis curves were observed at thoracic area (48.4%), at thoracolumbar area (27.4%) and at lumbar area(24.4%). 4) Right side curve was 59.7%, and left side curve was 40.3%. 5) After the 5 month exercise program for scoliosis, the Cobb's angle was significantly decreased. 6) There was no significant difference of Cobb's angle change respect to sex, grades, and scoliosis curve site. Results shown here indicates that an early detection and early exercise for scoliosis can result in decreased the Cobb's angle in elementary school children.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

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.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.27-34
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• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.33-41
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• 2024

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.729-737
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• 2020

Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.

• Journal of Environmental Health Sciences
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• v.44 no.5
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• pp.452-459
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• 2018

Objectives: We aimed to examine whether dental waste was being managed adequately at different types of dental institutions in City D in South Korea. Methods: The staff responsible for disinfection at 101 dental offices and clinics (six dentistry departments of general hospitals, 12 dental hospitals, and 83 dental clinics) was interviewed. Results: Solid suction pump waste was handled appropriately at four of the general hospital dentistry departments (66.7%), six dental hospitals (50.0%), and 15 dental clinics (18.1%). Solid spittoon waste was handled appropriately at four general hospital dentistry departments (66.7%), seven dental hospitals (58.3%), and 14 dental clinics (16.9%). Developer and fixer were handled appropriately by a subcontractor at two general hospital dentistry departments (100.0%), five dental hospitals (100.0%), and 24 dental clinics (75.0%). Impression materials were handled appropriately at four general hospital dentistry departments (66.7%), six dental hospitals (50.0%), and 11 dental clinics (13.3%). The plastic covers of intra-oral radiography films were handled appropriately at five general hospital dentistry departments (100.0%), eight dental hospitals (72.7%), and 22 dental clinics (30.1%). Conclusion: South Korea must implement detailed and specialized guidelines for the disposal of solid and general medical waste from dental institutions. Moreover, waste disposal training should be provided annually, and not only once every three years.