• 건강소식
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• v.35 no.4
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• pp.18-19
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• 2011

턱을 받침대에 대고 양손을 열중쉬어 자세로 한다. "숨을 크게 들이마시고 잠깐 참으세요."라는 방사선사의 말이 끝나자마자 검사가 끝난다. 흉부 X선 검사는 참으로 간단한 검사다. 이렇게 간단한 흉부 X선 검사를 통해서 폐와 심장을 비롯한 기관을 훤히 들여다 볼 수 있다.

• Journal of the Korean Society of Radiology
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• v.7 no.3
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• pp.165-173
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• 2013

The purpose of this study is to examine how much the movement at X-ray examinations like breathing or the positioning affects the image during chest or abdomen X-ray examination so as to create an image containing information as much as possible. The study method adopted is doing the X-ray in each of the states including breathing (inspiration & expiration) and movement in the standing chest PA X-ray and simple abdomen X-ray among the kinds of examination selected the most in hospitals and then evaluating them by applying the standards of image evaluation for each region. According to the study result, about the standing chest PA X-ray, the images taken at inspiration contain more information than those taken at expiration or having subtle movement during the examination. About the simple abdomen X-ray, the images taken at expiration contain more information than those taken at inspiration or movement. The above study results imply that regarding general X-ray examination, information we can find from the images may differ significantly according to the region examined, examination purpose, or movement during the examination like breathing.

• Journal of Chest Surgery
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• v.31 no.7
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• pp.730-734
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• 1998

Background: It is important to know the location, number, size and shape of bullae before thoracotomy or VATS bullectomy. Chest X-ray and chest CT may be used but with some limitation. The purpose of this study was to compare the diagnostic value of thoracography with that of chest X-ray in preoperative detection of bullae. Meterial and Method: Thoracography was performed by injection of non-ionic water-soluble dye into pleural space in 22 primary spontaneous pneumothoraces, which underwent thoracotomy or VATS bullectomy. Chest X-ray and thoracography were compared through operative finding. Results: Sensitivity and accuracy of thoracography(75% and 72.7%) were higher than those of chest X-ray(30% and 36.4%). However, specificity of thoracography(50%) was lower than that of chest X-ray (100%). There were no complications during or after thoracography. Conclusion: Thoracography is a safer and more useful method for preoperative detection of bullae when compared with chest X-ray.

• Proceedings of the Korean Society of Radiological Science
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• 1992.10a
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• pp.108.2-109
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• 1992

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.351-364
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• 2020

Missed lung cancers on chest radiograph (CXR) may delay the diagnosis and affect the prognosis. CXR is the primary imaging modality to evaluate the lungs and mediastinum in daily practice. The purpose of this article is to review chest radiographs for common blind spots and highlight the importance of various radiologic presentations in primary lung cancer to avoid significant diagnostic errors on CXR.

• Proceedings of the Korean Society of Radiological Science
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• 1995.05a
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• pp.121-121
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• 1995

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1334-1347
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• 2020

Coronavirus disease (COVID-19) has threatened public health as a global pandemic. Chest CT and radiography are crucial in managing COVID-19 in addition to reverse transcription-polymerase chain reaction, which is the gold standard for COVID-19 diagnosis. This is a review of the current status of the use of chest CT and radiography in COVID-19 diagnosis and management and anㄷ introduction of early representative studies on the application of artificial intelligence to chest CT and radiography. The authors also share their experiences to provide insights into the future value of artificial intelligence.

• Tuberculosis and Respiratory Diseases
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• v.43 no.3
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• pp.388-402
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• 1996

Background : To compare the diagnostic accuracies of High-resolution CT(HRCI) and chest radiography in the diagnosis of diffuse infiltrative lung disease(DILD). Methods : This study included ninety-nine patients with a diagnosis of acute or chronic DILD, representing 20 different diseases. Twelve normal subjects were included as control. The disease state was confirmed either pathologically or clinically. Radiographs and CT scans were evaluated separately by three independent observers without knowledge of clinical and pathologic results. The observers listed three most likely diagnoses and recorded degree of confidence. Results : The sensitivity of HRCT in the detection of DILD was 98.9% compared to 97.9% of chest radiography. Overall, a correct first-choice diagnosis was made in 48% using chest radiographs and in 60% using HRCT images. The correct diagnosis was among the top-three choices in 64% when chest radiographs were used, and in 75% when HRCT images were reviewed. Overally a confident diagnosis was reached more often with HRCT(55%) than with chest radiography(26%). The correct first-choice diagnosis increased remarkably when the HRCT was used in usual interstitial pneumonia, miliary tuberculosis, diffuse panbronchiolitis and lymphangitic carcinomatosis. Conclusion : HRCT is confirmed to be superior to conventional radiography in the detection and accurate diagnosis of DILD. HRCT is especially valuable in the diagnosis of usual interstitial pneumonia, miliary tuberculosis, diffuse panbronchiolitis, and lymphangitic carcinomatosis.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.265-283
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• 2022

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic un-hospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.