• Pediatric Infection and Vaccine
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• v.31 no.1
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• pp.113-121
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• 2024

Purpose: In response to the surge in coronavirus disease 2019 (COVID-19) omicron variant cases, we have implemented preemptive testing for preschool and school. The purpose is to quickly detect COVID-19 cases using a rapid antigen test (RAT) kit so that normal school activities can continue. Methods: The results entered in The Healthcare Self-Test App were merged with the information on the status of confirmed cases in the COVID-19 Information Management System by Korea Disease Control and Prevention Agency (KDCA) for preschool and school of students and staffs March 2 to May 1, 2022 to analyze the RAT positive rate and positive predictive value of RAT. Results: In preschool and school 19,458,575 people were tested, weekly RAT positive rate ranged from 1.10% to 5.90%, positive predictive value of RAT ranged from 86.42% to 93.18%. By status, RAT positive rate ranged from 1.13% to 6.16% for students, 0.99% to 3.93% for staffs, positive predictive value of RAT ranged from 87.19% to 94.03% for students, 77.55% to 83.10% for staffs. RAT positive rate by symptoms ranged from 76.32% to 88.02% for those with symptoms and 0.34% to 1.11% for those without symptoms. As a result of preschool and school RAT, 943,342 confirmed cases were preemptively detected, before infection spread in preschool and school. Conclusions: RAT was well utilized to detect confirmed cases at an early stage, reducing the risk of transmission to minimize the educational gap in preschool and school. To compensate for the limitations of RAT, further research should continue to reevaluate the performance of RAT as new strains of viruses continue to emerge. We will have to come up with various ways to utilize it, such as performing periodic and repeated RAT and parallel polymerase chain reaction.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.41-51
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• 2023

Purpose: This study compares and analyzes the image quality of 3D-CBCT(Cone Beam Computed-Tomography) and Gated CBCT according to baseline changes during SBRT(Stereotactic Body RadioTherapy) in lung cancer patients to find a useful CBCT method for correcting movement due to breathing Materials and methods : Insert a solid tumor material with a diameter of 3 cm into the QUASAR^TM phantom. 4-Dimentional Computed-Tomography(4DCT) images were taken with a speed of the phantom at period 3 sec and a maximum amplitude of 20 mm. Using the contouring menu of the computerized treatment planning system Eclipse^TM Gross Tumor Volume was outlined on solid tumor material. Set-up the same as when acquiring a 4DCT image using Truebeam STx^TM, breathing patterns with baseline changes of 1 mm, 3 mm, and 5 mm were input into the phantom to obtain 3D-CBCT (Spotlight, Full) and Gated-CBCT (Spotlight, Full) images five times repeatedly. The acquired images were compared with the Signal-to-Noise Ratio(SNR), Contrast-to-Noise Ratio(CNR), Tumor Volume Length, and Motion Blurring Ratio(MBR) based on the 4DCT image. Results: The average Signal-to-Noise Ratio, Contrast-to-Noise Ratio, Tumor Volume Length and Motion Blurring Ratio of Spotlight Gated CBCT images were 13.30±0.10%, 7.78±0.16%, 3.55±0.17%, 1.18±0.06%. As a result, Spotlight Gated-CBCT images according to baseline change showed better values than Spotligtht 3D-CBCT images. Also, the average Signal-to-Noise Ratio, Contrast-to-Noise Ratio, Tumor Volume Length and Motion Blurring Ratio of Full Gated CBCT images were 12.80±0.11%, 7.60±0.11%, 3.54±0.16%, 1.18±0.05%. As a result Full GatedCBCT images according to baseline change showed better values than Full 3D-CBCT images. Conclusion : Compared to 3D-CBCT images, Gated-CBCT images had better image quality according to the baseline change, and the effect of Motion Blurring Artifacts caused by breathing was small. Therefore, it is considered useful to image guided using Gated-CBCT when a baseline change occurs due to difficulty in regular breathing during SBRT that exposes high doses in a short period of time

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.203-212
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• 2024

The purpose of this study was to survey by the veterinary hospital Specialists (VHSs) and radiology students (RSs) for radiology curriculum development veterinary hospital (VH), and for veterinary hospital radiological technologists (VHRTs). VHSs were surveyed to regarding the basic information and radiological examination training, radiological examination experience, anatomy physiology, radiation safety management training, radiation biology training. RSs were surveyed to regarding the basic information and career paths, VH awareness, and VH-related department environments. The survey results were quantitatively entered into Excel and then analyzed using the SPSS ver. 26.0. The students were aged by 22.6 years old, and out of 171 students, male and female were 92 and 79 espectively. In employment career paths, 62.6% of all subjects responded that employment prospects at medical institutions were good. Employment prospects outside of medical institutions, VH had the highest number of students. Of the 83 students who responded that they wanted to work at a VH, 64 students liked animals, and 47 students the high potential for advancement. Of the 159 students who responded that there is potential for development of VH, 96.2% responded that it was due to the increase in companion animals. In the VH-related department environment, 94.7% responded that there was no related equipment, and 72.5% responded that the department needed to open animal care courses and 82.5% anatomy and physiology courses. 76.6% responded that they would be willing to take animal-related courses if they were offered. Among the 20 VHSAs, 4 had no experience in radiological examination of animals, 2 VHRTs, and 2 others. There were 7 people who had not received training in animal radiography, and 2 VHRTs had not received training in animal care and animal anatomy and physiology. This study is expected to be helpful in developing a radiology curriculum for VHRTs in the future.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.257-265
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• 2024

In this study, we applied AEC(Auto Exposure Control), which is used in many chest examinations, to evaluate whether medical devices inserted into the body affect the dose and image quality of chest images. After attaching three HIMD(Human implantable medical devices) to the ion chamber, the Monte Carlo methodology-based program PCXMC(PC Program for X-ray Monte Carlo) 2.0 was applied to measure the effective dose by inputting the DAP(Dose Ares Product) value derived from the Pacemaker and CRT and Chemoport Additionally, to evaluate image quality, we set three regions of interest and one noise region on the chest and measured SNR and CNR. The final study results showed significant differences in DAP and Effective dose. There was a significant difference between Pacemaker and CRT when AEC was applied and not applied. (p<0.05) When applied, the dose increased by 37% for Pacemaekr and 52% for CRT. Chemoport showed a 10% increase in effective dose depending on whether AEC was applied, but there was no significant difference. (p>0.05) In the image quality evaluation, there was no significant difference in image quality between all HIMD insertions and AEC applied or not. (p>0.05) Therefore, when the HIMD was inserted into the chest during a chest x ray and overlapped with the ion chamber sensor, the effective dose increased, and there was no difference in image quality even at a low dose without AEC. Therefore, when performing a chest X-ray examination of a patient with a HIMD inserted, it is considered that performing the examination without applying AEC is a method that can be considered to reduce the patient's radiation exposure.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• The korean journal of orthodontics
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• v.36 no.5
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• pp.339-348
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• 2006

Objective: With development of the skeletal anchorage system, orthodontic mini-implant (OMI) assisted on masse sliding retraction has become part of general orthodontic treatment. But compared to the emphasis on successful anchorage preparation, the control of anterior teeth axis has not been emphasized enough. Methods: A 3-D finite element Base model of maxillary dental arch and a Lingual tipping model with lingually inclined anterior teeth were constructed. To evaluate factors influencing the axis of anterior teeth when OMI was used as anchorage, models were simulated with 2 mm or 5 mm retraction hooks and/or by the addition of 4 mm of compensating curve (CC) on the main archwire. The stress distribution on the roots and a 25000 times enlarged axis graph were evaluated. Results: Intrusive component of retraction force directed postero-superiorly from the 2 mm height hook did not reduce the lingual tipping of anterior teeth. When hook height was increased to 5 mm, lateral incisor showed crown-labial and root-lingual torque and uncontrolled tipping of the canine was increased.4 mm of CC added to the main archwire also induced crown-labial and root-lingual torque of the lateral incisor but uncontrolled tipping of the canine was decreased. Lingual tipping model showed very similar results compared with the Base model. Conclusion: The results of this study showed that height of the hook and compensating curve on the main archwire can influence the axis of anterior teeth. These data can be used as guidelines for clinical application.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.2
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• pp.87-92
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• 2015

Purpose When the patients takes myocardial perfusion SPECT using $^{201}Tl$, the operator gives the patients an injection of $^{201}Tl$. But the uniformity correction map in SPECT uses $^{99m}Tc$ uniformity correction map. Thus, we want to compare the image quality when it uses $^{99m}Tc$ uniformity correction map and when it uses $^{201}Tl$ uniformity correction map. Materials and Methods Phantom study is performed. We take the data by Asan medical center daily QC condition with flood phantom including $^{201}Tl$ 21.3 kBq/mL. After postprocessing with this data, we analyze CFOV integral uniformity(I.U) and differential uniformity(D.U). And we take the data with Jaszczak ECT Phantom by American college of radiology accreditation program instruction including $^{201}Tl$ 33.4 kBq/mL. After post processing with this data, we analyze spatial Resolution, Integral Uniformity(I.U), coefficient of variation(C.V) and Contrast with Interactive data language program. Results In the flood phantom test, when it uses $^{99m}Tc$ uniformity correction map, Flood I.U is 3.6% and D.U is 3.0%. When it uses $^{201}Tl$ uniformity correction map, Flood I.U is 3.8% and D.U is 2.1%. The flood I.U is worsen about 5%, but the D.U is improved about 30% inversely. In the Jaszczak ECT phantom test, when it uses $^{99m}Tc$ uniformity correction map, SPECT I.U, C.V and contrast is 13.99%, 4.89% and 0.69. When it uses $^{201}Tl$ uniformity correction map, SPECT I.U, C.V and contrast is 11.37%, 4.79% and 0.78. All of data are improved about 18%, 2%, 13% The spatial resolution was no significant changes. Conclusion In the flood phantom test, Flood I.U is worsen but Flood D.U is improved. Therefore, it's uncertain that an image quality is improved with flood phantom test. On the other hand, SPECT I.U, C.V, Contrast are improved about 18%, 2%, 13% in the Jaszczak ECT phantom test. This study has limitations that we can't take all variables into account and study with two phantoms. We need think about things that it has a good effect when doctors decipher the nuclear medicine image and it's possible to improve the image quality using the uniformity correction map of other radionuclides other than $^{99m}Tc$, $^{201}Tl$ when we make other nuclear medicine examinations.

• Journal of agricultural medicine and community health
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• v.29 no.1
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• pp.65-75
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• 2004

Objectives: Immunizations have been one of the most effective measures preventing from infectious diseases. It is quite important national infectious disease prevention policy to keep the immunizations rate high and monitor the immunizations rate continuously. To do this, Korean CDC introduced the National Immunization Registry Program(NIRP) which has been implementing since 2000 at the Public Health Centers(PHC). The National Immunization Registry Program will be near completed after sharing, connecting and transfering vaccination data between public and private sector. The aims of this study was to evaluate the immunization module of non-chart system in private clinic with health information system of public health center(made by POSDATA Co., LTD) and immunization registry program(made by BIT Computer Co., LTD). Methods: The analysis and survey were done by specialists in medical, health field, and health information fields from 2001. November to 2002. January. We made the analysis and recommendation about the immunization module of non-chart system in private clinic. Results and Conclusions: To make improvement on immunization module, the system will be revised on various function like receipt and registration, preliminary medical examination, reference and inquiry, registration of vaccine, print-out various sheet, function of transfer vaccination data, issue function of vaccination certification, function of reminder and recall, function of statistical calculation, and management of vaccine stock. There are needs of an accurate assessment of current immunization module on each private non-chart system. And further studies will be necessary to make it an accurate system under changing health policy related national immunization program. We hope that the result of this study may contribute to establish the National Immunization Registry Program.