• 간호행정학회지
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• 제11권1호
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• pp.59-66
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• 2005

Purpose: The purpose of this study was to identify the effects images about medical organizations and the quality of service on customer satisfaction of obs and gyn patients. Method: The subjects of this study were selected conveniently 220 women among obstetrics and gynecology outpatients( who visited H- doctor's office of the first medical organ, P- hospital of the second medical organization, Chospital of the third medical organization). The data were collected from August, 20th 2004 using structured questionaires which included modified form of SERVPERF and customer's satisfaction scale by oliver & swan(1989), and modified form of image scale by kang(1997). Results: Image of hospital(45.3%), visiting frequencies(9.3%), service provider (3.9%) and convenient use(1.2%) of the quality of medical service were significant predictors to explain customer's satisfaction. Conclusion: To increase customer's satisfaction of obs and gyn patients, it is required to developed strategies that improve image of hospital and the quality of service to service provider and convenient use of hospital.

• Nuclear Medicine and Molecular Imaging
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• 제40권1호
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• pp.51-52
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• 2006

A 39-year-old female patient who had undergone a total thyroidectomy for a papillary thyroid carcinoma underwent a whole body scan with I-131. The I-131 scan was performed 72 hours after administering 185 MBq (5 mCi) of an I-131 solution. The anterior image of the head, neck, and upper chest showed multiple areas of increased uptake in the mediastinal area considering of functional metastasis. However, radioactivity was not evident in the image taken after removing her clothes and muffler. The image obtained after placing the muffler on the pallet showed that the radioactivity was still present. It is well known that artifacts on an I-131 scan can be produced by styling hair sputum, drooling during sleep, chewing gum, and paper or a cloth handkerchief that is contaminated with the radioactive iodine from either perspiration or saliva. This activity might be mistaken for a functional metastasis. Therefore, it is essential that an image be obtained after removing the patient's clothes. In this study, artifacts due to a contaminated muffler on the I-131 scan were found. These mimicked a functional metastasis of the mediastinal area in a patient with a papillary thyroid carcinoma.

• 보건의료산업학회지
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• 제7권2호
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• pp.53-67
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• 2013

This study aims to analyze the effect of quality of health care on perceived value, patient satisfaction and revisit intention. Especially, it was focused on outdoor environment, admission procedure, hospital image, service quality of physicians that patients perceived. For inpatients, hospital image and service quality of medical technicians have an effect on perceived value. Service quality of physicians has an effect on the patient satisfaction. For outpatients, hospital image and service quality of physicians and medical technicians have an effect on perceived value. Outdoor environment, hospital image, service quality of physicians and medical technicians, and perceived value have an effect on patient satisfaction. Perceived value and patient satisfaction have an effect on revisit intention. They should evaluate customer satisfaction on their services and analyze various factors that affect on it to improve specialty hospitals.

• Journal of Korean Neurosurgical Society
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• 제37권6호
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• pp.453-455
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• 2005

Two young women were brought to the Emergency room with generalized tonic and clonic seizures. Seizure developed seven and ten days after delivery respectively without the clinical signs of pre-eclampsia throughout the pregnancies. Magnetic resonance(MR) image of the brain showed characteristically symmetrical abnormal signals in the parietal and occipital regions. After several days of medical treatment, they were discharged without neurologic sequelae and follow-up MR images taken three months after discharge showed complete disappearance of the previous abnormal signals.

• Korean Journal of Radiology
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• 제22권7호
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제16권3호
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• pp.27-36
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• 2010

Recently general hospitals in Korea have been actively remodeled. Remodeling is required to extend the hospitals' area and to meet the new medical demand. Eventually it aims for achieving ideal healing environment. Seoul National University Bundang Hospital has also developed the schematic design to open the new hospital in 2012. This large scale of extension is the first step in its expected remodeling cycle. It is essential for the extension of hospital to create an architectural system on the basis of function, and to keep the balance with both the existing buildings and natural context simultaneously. To connect the existing hospital and the new hospital, a hospital street should be designed to make it function as a main pedestrian spine. Space design marketing of general hospital is effective in promoting the hospital image. It can be realized by emphasizing hospital identity through combining cultural program and commercial facilities. Developing hospital design should be encouraged under the EBD (Evidence Based Design) concept spread in USA.

• 대한디지털의료영상학회논문지
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• 제9권2호
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• pp.17-21
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• 2007

The purpose to recognize change of average pixel value of acquisition image by control panel's density and right set up method of speed (sensitivity) and exposure dose(mAs) change that dose in purpose digital flatpanel-detector. X -ray generator DHF-158H2(Hitachi, Japan). Detector CXDI 4OG(Canon, Japan), 12 : 1 grid and exposure ray 135 kVp, 250 mA, 10 ms. focus-detector distance 180 cm and used AEC mode. DICOM reflex analysis program used image J that is digital reflex analysis program that offer in United States America National Health Center(National Institutes of Health : NlH) phantom used chest phantom(Anthromorphic : Flukebrome.medicaI USA). An experiment chest phantom that consist by formation equivalence material use because density value( -3${\sim}$+3) in X-ray control panel and seep that is speed step(slow, medium, fast) each control experimentalize. image analysis reflex neted through an experiment using image j each image compare. These was change in dose according to slow, medium, fast and density's change in an experiment result. According to detector sensitivity and density condition set, dose was relationship dissimilarity 500% from 200%. The dose came highest when is density +3 to slow. and dose more increases gray scale's extent could know that rise. Could know whether how equipment set is important through this experiment. cause of disease which change by digital radiography system forward is thought to increase more, it is considered that suitable education by this and continuous interest about equipment need absolutely.

• 한국의료질향상학회지
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• 제20권1호
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• pp.60-73
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• 2014

Objectives: This study aims at decreasing spatial dose rate through work improvement whilst spatial dose rate is the cause of increasing personal exposure dose which occurs in the process of handling radioisotope. Methods: From February 2013 until July 2013, divided into "before" and "after" the improvement, spatial dose rate in laboratory of nuclear medicine was measured in gamma image room, PET/CT-1 image room, and PET/CT-2 image room as its locations. The measurement time was 08:00, 12:00 and 17:00, and SPSS 21.0 USA was opted for its statistical analysis. Result: The spatial dose rate at distribution worktable, injection table, the entrance to the distribution room, and radioisotope storage box, which had showed high spatial dose rate, decreased by more than 43.7% a monthly average. The distribution worktable, that had showed the highest spatial dose rate in PET/CT-1 image room, dropped the rate to 42.3% as of July. The injection table and distribution worktable in the PET/CT-2 image room also showed the decline of spatial dose rate to 89% and 64.4%, respectively. Conclusion: By improving distribution process and introducing proper radiation shielding material, we were able to drop the spatial dose rate substantially at distribution worktable, injection table, and nuclide storage box. However, taking into account of steadily increasing amount of radioisotope used, strengthening radiation related regulations, and safe utilization of radioisotope, the process of system improvement needs to be maintained through continuous monitoring.

• 핵의학기술
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• 제17권1호
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• pp.71-75
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• 2013

현재 PET/CT 검사에서 저선량 CT를 이용한 감쇠 보정을 사용하고 있다. 하지만 환자 호흡으로 인한 횡격막 부근의 저선량 CT 영상과 Emission 영상의 불일치로 감쇠 보정영상에서 Artifact가 발생하는 경우가 있다. 본 연구는 보정방법 중 CTAC Shift를 이용하여 환자의 호흡에 의한 Artifact의 감소를 연구했다. 2012년 3월부터 9월까지 PET/CT Discovery 600 (GE Healthcare, MI, USA) 장비를 이용하여 호흡에 의한 Artifact가 발생한 환자 30명을 대상으로 했다. Artifact가 발생한 환자는 횡격막 부분을 추가검사하였고, 전신 검사 시 Artifact가 발생한 영상을 CTAC Shift를 이용하여 보정했다. Artifact 발생 영상, 추가 검사영상, CTAC Shift 보정 영상의 육안적 평가는 핵의학 전문의 1명과 5년 이상 근무한 방사선사 4명에 의해 1-5점으로 나누어 평가했다. 또한 각 영상의 표준섭취화 계수를 ANOVA를 이용하여 비교했다. Artifact 발생 영상에 비해 추가 검사 영상과 CTAC Shift 보정 영상은 육안적 평가에서 상대적으로 높은 점수를 받았다. 추가 검사 영상과 CTAC Shift 보정 영상은 표준섭취화 계수의 ANOVA 결과, 높은 상관관계를 갖고 있으며, 유의한 차이를 보이지 않았다. PET/CT 검사 시 환자의 호흡에 의한 Artifact가 발생 할 경우 추가 검사로 인한 검사 시간이 증가하여 환자의 불편 뿐 아니라 피폭도 증가한다. 하지만 추가 검사 없이 CTAC Shift를 이용하여 보정된 영상을 획득한다면 불필요한 피폭 및 추가 검사도 감소하며, 정확한 진단에 도움을 줄 것으로 사료된다.

• Advances in nano research
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• 제12권2호
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.