• Journal of Chest Surgery
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• v.33 no.9
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• pp.752-755
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• 2000

Isolated congenital aneurysm of the left atrium with intact pericardium is a rate anomaly, which usually presents with arrhythmia, cerebral embolism or abnormalities on routine chest X-ray. Surgery is indicated in most cases to eliminate a potential source of systemic emboli and arrhythmias. A 42-year-old woman having cervical cancer, she was suspected of having a left atrial aneurysm on review of chest X-ray and confirmed by echocardiography and cardiac catheterization. Surgical resection of Left atrial aneurysm was achieved without complication using median sternotomy with cardiopulmonary bypass. The postoperative course was uneventful.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.31-32
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• 2021

신종 코로나바이러스 감염증(Coronavirus disease 2019; COVID-19)이 빠르게 확산됨에 따라 세계적인 전염병 대유행인 팬데믹(Pandemic)으로 선언되었다. 감염자들은 꾸준히 증가하고 있고 최근에는, 무증상 감염자들이 나타나고 있어 의심 환자를 조기에 판단하고 선별할 수 있는 기술이 필요하다. 본 논문에서는 흉부 방사선 검사(chest Radiography; CXR) 영상을 딥러닝(Deep Learning)하여 정상인, 폐렴 환자, 코로나바이러스 감염자를 분류할 수 있도록 한다.

• Korean Journal of Radiology
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• v.21 no.5
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• pp.511-525
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• 2020

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.693-704
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• 2024

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

• Tuberculosis and Respiratory Diseases
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• v.43 no.1
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• pp.113-116
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• 1996

A previously healthy 59-year old male patient was admitted due to cough and abnormal chest x-ray. Cough started 5 months ago and persisted. Two months before admission, abnormality in chest PA was detected. He had no symptom other than cough. He was nonsmoker and physical examination revealed no abnormal finding. His chest X-ray showed ill-defined $2{\times}1\;cm$ ovoid infiltration in left middle lung field. On chest computed tomography, it was located in the subpleural region of posterobasal segment of left lower lobe. Mediastinal lymphadenopathy was absent. Blood test and sputum examination were not diagnostic. Fluoroscopy-guided percutaneous needle biopsy revealed pulmonary cryptococcosis. After central nervous system involvement was excluded by spinal tap, oral ketoconazole therapy was started. The lesion decreased in size after 8 weeks of therapy and almost disappeared on follow-up chest X-ray 4 months later.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.65-70
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• 2020

PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.1
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• pp.5-8
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• 2010

Opened a court in February 10, 2006, a rule of safety management of the diagnosis radiation system was promulgated for safety of the radiation worker, patients and patients' family members. The purpose of this rule is to minimize the risk of being exposed to radiation during the process of handling X-ray. For this reason, we manufactured shielding device of mobile X-ray unit collimator for diminution of skin dose. Shielding device is made to a thickness of Pb 0.375mm. For portable chest radiography, we measured skin dose 50cm from center ray to 200cm at intervals of 20cm by Unfors Xi detector. As a result, a rule of safety management of the diagnosis radiation system has been strengthened. But there are exceptions, such as ER, OR, ICU to this rule. So shielding device could contribute to protect unnecessary radiation exposure and improve nation's health.

• Journal of radiological science and technology
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• v.20 no.2
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• pp.49-55
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• 1997

This study was carried out to investigate radiographical and operating conditions of X-ray units and exposure doses to patients during chest radiography, so that the results could provide basic data used for reducing the exposure dose and for providing the diagnostic information with better quality. The conditions and exposure doses of 100 X-ray units mainly used for chest radiography were examined and also 100 radiological technologists mainly handling those apparatus at 76 medical facilities in Pusan were surveyed using a questionnaire from October 1 to December 31 in 1995. The following results were obtained from the study : 1. It was found that most units were capable of taking a high tube voltage radiography by showing 67% of the units equipped with the maximum tube voltage of 150 kV, 94% with more than 500 mA for the rating capacity and 85% with the full wave type of a signal phase. 2. For actual chest radiographical conditions, however, 80% of the units were operated at $60{\sim}100\;kVp$ and only 14% at 100 kVp and over for the high tube voltage. 3. The average exposure time was less than 0.1 second, and eighty four percent of the units adapted the X-ray tube currents ranging from 200 to 300 mA, 80% the focus-film distances between 180 and 210 cm, and 63% the focus sizes of more than 2.0 mm. 4. Most units(98%) employed additional filters made of aluminum, 75% the thickness of filters less than 2.0 mm, and only 2 units the compound filters. 5. Ortho chromatic system was only adopted in 13% of screen film system for the units, and 73% used the grid ratio at 8 : 1 for the low tube voltage during chest radiography. 6. The average exposure dose of all X-ray units during chest radiography was $371\;{\mu}Sv$ with a difference of about 16 times between the minimum to the maximum, and $386\;{\mu}Sv$ both at hospitals and at health centers, followed by $380\;{\mu}Sv$ at general hospitals and $263\;{\mu}Sv$ at university hospitals without showing any statistically significant differences. In conclusion, since patients during chest radiography at medical facilities in Pusan exposed to high levels of radiation, it is recommended that appropriate added filters and grids necessary for the high tube voltage radiography and high-speed screen systems should be adopted and used as soon as possible in order to reduce exposure dose to the patients.

• Journal of radiological science and technology
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• v.16 no.1
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• pp.57-65
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• 1993

Asymetric system have been introduced in these years by KODAK company nam of Insight system for the purpose of improve the chest image. We have had a problem of chest radiology that it is very difficult to visualize the lung field and modiastinal region at one shot. That's why we are the RT using the technique of high voltage hard quality radiography in chest radiography. Also it is known the c-type wide latitude film can lift up the density of mediastinal structures. Authors investigated the photographic characteristics and physical structure of Insight system. Method 1. Investigated the structure of Emulsion layer. Calculated the particle size of Insight system using SEM(Scanning Electron Microscope). 2. Photographic characteristics has been compared the Insight system with the ortho KM/MG combination in $60{\sim}120kV$ range. Results 1. The particle size of backside film were investigated about 2 times larger that of front side film. 2. The front and backscreen's thickness ratio was detected 1 : 3.87, that the backscreen's thickness was thicker than frontscreen. 3. At the view point of photographic characteristics the frontside of insight system make up the contrast, backside make up the density at low exposure lesion.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.