• International Journal of Oral Biology
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• v.38 no.3
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• pp.87-92
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• 2013

Periodontal inflammation increases the risk of tooth loss, particularly in cases where there is an associated loss of alveolar bone and periodontal ligament (PDL). Histological and morphometric evaluation of periodontal inflammation is difficult. Especially, the lengths of the periodontal ligament and interdental alveolar bone space have not been quantified. A quantitative imaging procedure applicable to an animal model would be an important clinical study. The purpose of this study was to quantify the loss of alveolar bone and periodontal ligament by evaluation with micro-computed tomography (micro-CT). Another purpose was to investigate differences in infections with systemic E. coli LPS and TNF-${\alpha}$ on E. coli lipopolysaccharide (LPS) in loss of alveolar bone and periodontal ligament model on mice. This study showed that linear measurements of alveolar bone loss were represented with an increasing trend of the periodontal ligament length and interdental alveolar process space. The effects of systemic E. coli LPS and TNF-${\alpha}$ on an E. coli LPS-induced periodontitis mice model were investigated in this research. Loss of periodontal ligament and alveolar bone were evaluated by micro-computed tomography (micro-CT) and calculated by the two- and three dimensional microstructure morphometric parameters. Also, there was a significantly increasing trend of the interdental alveolar process space in E. coli LPS and TNF-${\alpha}$ on E. coli LPS compared to PBS. And E. coli LPS and TNF-${\alpha}$ on E. coli LPS had a slightly increasing trend of the periodontal ligament length. The increasing trend of TNF-${\alpha}$ on the LPS-induced mice model in this experiment supports the previous studies on the contribution of periodontal diseases in the pathogenesis of systemic diseases. Also, our findings offer a unique model for the study of the role of LPS-induced TNF-${\alpha}$ in systemic and chronic local inflammatory processes and inflammatory diseases. In this study, we performed rapidly quantification of the periodontal inflammatory processes and periodontal bone loss using micro-computed tomography (micro-CT) in mice.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.387-391
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• 2015

Background: Fluorine-18 deoxyglucose positron emission tomography computed tomography (18F-FDG-PET/CT) and bone scintigraphy (BS) are widely used for the detection of bone involvement. The optimal imaging modality for the detection of bone metastases in hormone receptor positive (+) and negative (-) groups of breast cancer remains ambiguous. Materials and Methods: Sixty-two patients with breast cancer, who had undergone both 18F-FDG-PET/CT and BS, being eventually diagnosed as having bone metastases, were enrolled in this study. Results: 18F-FDG-PET/CT had higher sensitivity and specificity than BS. Our data showed that 18F-FDGPET/CT had a sensitivity of 93.4% and a specificity of 99.4%, whiel for BS they were 84.5%, and 89.6% in the diagnosis of bone metastases. ${\kappa}$ statistics were calculated for 18F-FDGPET/CT and BS. The ${\kappa}$-value was 0.65 between 18F-FDG-PET/CT and BS in all patients. On the other hand, the ${\kappa}$-values were 0.70 in the hormone receptor (+) group, and 0.51 in hormone receptor (-) group. The ${\kappa}$-values suggested excellent agreement between all patient and hormone receptor (+) groups, while the ${\kappa}$-values suggested good agreement in the hormone receptor (-) group. Conclusions: The sensitivity and specificity for 18F-FDG-PET/CT were higher than BS in the screening of metastatic bone lesions in all patients. Similarly 18F-FDG-PET/CT had higher sensitivity and specificity in hormone receptor (+) and (-) groups.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.27-35
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• 2005

Recently, despite of the rapid progress of information technology in the medical and health fields, the development and management of problem sets about medical and education contents related with radiological technologist has been still achieved by manual and offline method using document editor. In this study, the unique web-based problem management system is designed and implemented. That system can efficiently manage and present various kind of problem set about integrated education and personal license without time and space limitations in order to improve the efficiency of supplementary training and to obtain the professional license for CT radiological technologist. The proposed system is composed of administration module and user module. The former supports several functions such as problem creation, problem categorization, user management, and adjustment of leveled assessment. On the other hand, the latter functions examination applying , problem retrieval, personal score retrieval, and interpretation viewing, and so on. In addition, our system is expected as a useful and practical system which provides problem interpretation and analysis of score results after applying for the examination. It can elevate ability of learning and information interchange among them preparing for CT professional radiological technologist licensing examination

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• Imaging Science in Dentistry
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• v.44 no.4
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• pp.301-305
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• 2014

Purpose: The purpose of this study was to compare the accuracy of postoperative maxillary cyst (POMC) diagnosis by panoramic radiographs versus computed tomography (CT) and by oral and maxillofacial radiologists versus non-specialists. Materials and Methods: Sixty-five maxillary sinuses with POMCs and 63 without any lesion were assessed using panoramic radiographs and CT images by five oral and maxillofacial radiologists and five non-specialists on a five-point scale. The areas under receiver operating characteristic (ROC) curves were analyzed to determine the differences in diagnostic accuracy between the two imaging modalities and between the two groups of observers. The intra-observer agreement was determined, too. Results: The diagnostic accuracy of CT images was higher than that of panoramic radiographs in both groups of observers (p<0.05). The diagnostic accuracy of oral and maxillofacial radiologists for each method was higher than that of non-specialists (p<0.05). Conclusion: The use of CT improves the diagnosis of POMC, and radiological training and experience leads to more accurate evaluation.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.317-325
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• 2021

In this paper, the degree of reflection of SW·AI elements and CT elements was investigated and analyzed for a total of 44 textbooks of Korean, social, moral, mathematics and science textbooks based on the 2015 revised curriculum. As a result of the analysis, most of the activities of data collection, data analysis, and data presentation, which are ICT elements, were not reflected, and algorithm and programming elements were not reflected among SW·AI content elements, and there were no abstraction, automation, and generalization elements among CT elements. Therefore, in order to effectively implement SW·AI convergence education in elementary school subjects, we will expand ICT utilization activities to SW·AI utilization activities. Training on the understanding of SW·AI convergence education and improvement of teaching and learning methods using SW·AI is needed for teachers. In addition, it is necessary to establish an information curriculum and secure separate class hours for substantial SW·AI education.