• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.77-85
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• 2022

The purpose of this study was to investigate the status of infection control of angiography room workers through a survey, and to find out their awareness and performance. A survey was conducted from January 3 to February 28, 2022 on 126 workers working in angiography room of 10 hospitals at or above general hospital level located in Busan City. The questionnaire consists of 10 general characteristics of the subject and 56 items in total, divided into 4 main items of infection control in an angiography room: infection control system in a medical institution, personal hygiene, angiography room environment, and angiography room equipment. was measured on a Likert 5-point scale. Data analysis was performed statistically using SPSS for WindowTM release 25.0. t-test and one-way ANOVA were used to analyze the awareness and performance of each domain according to general characteristics, and Pearson correlation analysis was performed for the relationship between variables. As a result, the awareness level was higher than the performance level in all areas, indicating that the performance level was lower than the awareness level. In addition, awareness and performance showed a positive correlation, suggesting that the degree of awareness of workers is an important variable in infection control that has a significant effect on performance. Therefore, for effective and systematic infection control, workers in angiography room must improve the performance of infection control. In order to do that, infection control education is needed, and it is judged that infection control guidelines for angiography room should be systematized in the future.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.32-35
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• 2016

Purpose The goal for this study is to figure out that medical staff except Nuclear Medicine Department could be exposed to radiation from the patients who take Nuclear Medicine examination. Materials and Methods Total 250 patients (Bone scan 100, Myocardial SPECT 100, PET/CT 50) were involved from July to October in 2015, and we measured patient dose rate two times for every patients. First, we checked radiation dose rate right after injecting an isotope (radiopharmaceutical). Secondly, we measured radiation dose rate after each examination. Results In the case of Bone scan, dose rate were $0.0278{\pm}0.0036mSv/h$ after injection and $0.0060{\pm}0.0018mSv/h$ after examination (3 hrs 52 minutes after injection on average). For Myocardial SPECT, dose rate were $0.0245{\pm}0.0027mSv/h$ after injection and $0.0123{\pm}0.0041mSv/h$ after examination (2 hrs 09 minutes after injection on average). Lastly, for PET/CT, dose rate were $0.0439{\pm}0.0087mSv/h$ after examination (68 minutes after injection on average). Conclusion Compared to Nuclear Safety Commission Act, there was no significant harmful effect of the exposure from patients who have been administered radiopharmaceuticals. However, we should strive to keep ALARA(as low as reasonably achievable) principle for radiation protection.

• Journal of radiological science and technology
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• v.30 no.2
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• pp.129-138
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• 2007

The Purpose of this study is to suggest the basic data for making good quality image and maintaining equipment homeostasis by accepting image quality evaluation and radiation dose evaluation in Multi-detector CT. In this study we surveyed 14 CT equipments in Seoul. The results obtained were as follows ; CT number was $0.56{\pm}0.70\;HU$. Noise was $0.39{\pm}0.09\;HU$. Uniformity was $1.08{\pm}0.52\;HU$. High contrast resolution was $0.48{\pm}0.05\;mm$ and low contrast resolution was $3.65{\pm}1.16\;mm$. For CTDI, the central part and the peripheral part of head phantom were $43.2{\pm}15.4\;mGy$ and $45.6{\pm}17.5\;mGy$, respectively. For body phantom, the central part and the peripheral part of head phantom were $13.5{\pm}4.5$ and $29.2{\pm}10.2\;mGy$, respectively. CTDIw was $44.8{\pm}16.8\;mGy$ and CTDIw/100 mAs was $18.8{\pm}5.3\;mGy$ using head phantom. CTDIW was $24.0{\pm}8.3\;mGy$ and CTDIw/100 mAs was $10.1{\pm}2.5\;mGy$ using body phantom. Therefore, CT number, noise, high contrast resolution, low contrast resolution, CTDI, CTDIw and CTDIw/100 mAs of MDCT were showed excellently in all equipments.