• Journal of the Korean Society of Radiology
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• v.12 no.7
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• pp.827-832
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• 2018

Research on effective dose analysis of actual conditions of use based on large data is scarce. In this study, the exposure conditions of Chest X-Ray examinations used by 324 medical institutions in Korea were calculated and evaluated using computer simulations. As a result of the experiment, the effective dose in the low energy parameter bands was 0.024 mSv, followed by spleen, adrenal glands, and lung. The effective dose in the high-energy exposure parameter band was 0.123 mSv, followed by height, spleen and adrenal glands. The effective dose was 0.017 mSv when the optimal conditions considered the quality and exposure proposed in Park's study were used. The results of the study will be a reference for chest X-rays and will help reduce patient exposure.

• Journal of radiological science and technology
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• v.13 no.1
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• pp.19-24
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• 1990

In appilcation of wide latitude HR-C film to chest x-ray examination, former x-ray diagnosis area is larger and diagnostic information has great deal of promotion. HR-C film is compare to former x-ray film is larger latitude and density level is small, reading is very easily. Especially, high estimate that is in characteristic curve linearity of toe part is good, contrast of low density made good shape and not good describe to overlap is diagnostic information increase mediastinum portion etc.

• Journal of Chest Surgery
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• v.26 no.8
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• pp.654-660
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• 1993

The incidence of pulmonary leiomyosarcoma is very rare as a primary lung tumor. Usually, pulmonary leiomyosarcoma arise from the smooth muscle present in the bronchi or blood vessles. We had experienced two cases of primary pulmonary leiomyosarcoma. The first case was 28-year old male patient who had been in good health until admission, when he experienced an episode of dyspnea and sudden hemoptysis. The chest X-ray film revealed a large round tumor mass in left lower lobe measuring 6.5x9.5x5.3cm in dimension. On physical examination,the patient was friction rub and rales on the left lower chest and postoperative course was smooth and non-eventful. Emergency left lower lobectomy was performed due to repeated hemoptysis. Chemotheraphy was done postoperatively as an adjuvant therapy.The second case was 52-year-old man who had been well prior to admission, when recently he noticed a abrupt growing tendency of old pulmonary coin lesion in right lower lobe on routine physical examination. Since 1968, small round mass was gradually enlarged very slowly, during recent one year interval, the tumor mass was enlarged abruptly as twice in size on chest X-ray. Bronchoscopic examination revealed no specipic findings. Right lower lobectomy was performed and pathologic examination was answered as primary leiomyosarcoma without lymph node metastasis. Postoperative course was smooth, except local wound infection.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.794-798
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• 2009

We introduce an implementation of plug-ins on PLUTO. These plug-ins discriminate inflammatory nodules from other types of nodules in chest X-ray CT images. The PLUTO is a common platform for computer-aided diagnosis systems on Microsoft Windows series and it is easy to add new functions as plug-ins. We coded two plug-ins. One of the them calculates features based on medical knowledge. The other plug-in calculates parameters to classify the type of nodules, and it also classifies nodules into inflammatory nodules and others using SVM. These plug-ins are coded using MIST library which is produced at Nagoya University, Japan. In our previous study, the MIST library was parallelized, so that we can utilize a number of CPUs to calculate features and SVM learning/classifying depending on the amount of computation. Using these plug-ins, it became easy to extract features to discriminate inflammatory nodules from other types of nodules and to change parameters for feature extraction and SVM learning/classifying with GUI interface. The accuracy of the classifying result is 100% with 78 solid nodules which contains 43 inflammatory nodules and 35 other type of nodules.

• Journal of radiological science and technology
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• v.45 no.3
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• pp.205-211
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• 2022

This study aimed to provide basic data for 3D printing in the medical health field by developing upper wear fixation device (UWFD), an auxiliary device for shortening chest AP examination time on emergency room beds and non-contact with patients. The standard of hooks was modeled according to the bed frame using the Autodesk Fusion 360. It was printed with Form2 (Formlabs, Somerville, MA, USA), as SLA (stereo lithography apparatus) method, and was washed and hardened using Form Wash and Form Cure. The completed UWFD conducted an online survey on 4 items of stability, convenience, availability, preference and general characteristics. The total stability average was 3.93±0.80, the total convenience average was 3.93±0.68, the total availability average was 4.01±0.89, and the total preference average was 3.80±1.08. This study was significant in suggesting improvements in the general X-ray examination process in the emergency room by designing and making aids to easily fixing the patient's top to the frame of the emergency bed while meeting promptness and non-contact with the patient.

• Clinical and Experimental Pediatrics
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• v.58 no.3
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• pp.108-111
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• 2015

DiGeorge syndrome is an immunodeficient disease associated with abnormal development of 3rd and 4th pharyngeal pouches. As a hemizygous deletion of chromosome 22q11.2 occurs, various clinical phenotypes are shown with a broad spectrum. Conotruncal cardiac anomalies, hypoplastic thymus, and hypocalcemia are the classic triad of DiGeorge syndrome. As this syndrome is characterized by hypoplastic or aplastic thymus, there are missing thymic shadow on their plain chest x-ray. Immunodeficient patients are traditionally known to be at an increased risk for malignancy, especially lymphoma. We experienced a 7-year-old DiGeorge syndrome patient with mediastinal mass shadow on her plain chest x-ray. She visited Severance Children's Hospital hospital with recurrent pneumonia, and throughout her repeated chest x-ray, there was a mass like shadow on anterior mediastinal area. We did full evaluation including chest computed tomography, chest ultrasonography, and chest magnetic resonance imaging. To rule out malignancy, video assisted thoracoscopic surgery was done. Final diagnosis of the mass which was thought to be malignancy, was lymphoproliferative lesion.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Journal of radiological science and technology
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• v.1 no.1
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• pp.44-54
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• 1978

This study was carried out with statistical materials during the last two years of period from Jan. 1975 to Dec. 1976 which presented at radiologic department of 5 hospitals in Seoul City. The primary purpose of this study was to obtained more detailed informations related to the activities of radiologic technologists in diagnostic X-Ray examinations part and to provide some basic materials for managements in activities of then and manpower managements of their organization and practice. From the results of this study, the following conclusions were obtained [1] During two year from the January of 1975 to the December of 1976, total number of case in X-ray examinations were 464,830 case and 22,029 case in 1975 and 24,461 in 1976. And ratio of icreased in X-Ray examinations by year was 11.09 per cent. [2] Regarding the examined portion of X-Ray examination, a great propotion was chest examination as 56.88 per cent. [3] An average, the required time per case in X-Ray exam. was 9.28 minutes and make used of 1.94 sheets of X-Ray film per case in radiography. [4] An average, ratio of increased in X-Ray film by year was 12.71 per cent and ratio of failed film in it was 2.23 per cent. [5] The frequency rate of film size showed the highest distribution of $8"{\times}10"$(28.17%) and the highest distribution of X-Ray film by month was July(8.93%). [6] An average, the amount of activities per a diagnostic X-Ray equipment was 34.92 case and make used of 67.81 sheets of X-Ray film in a day. [7] The mean number of case in X-Ray examinations by radiologic technologists was 29.29 cases and make used of 56.87 sheets of X-Ray film in a day. Also, the average number of case was reading by radiologists was 32.42 case and 62.97 sheets of X-Ray film in a day.