• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.540-547
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• 2021

Background: Indoor air pollutants are caused by a number of factors, such as coming in from the outside or being generated by internal activities. Typical indoor air pollutants include nitrogen dioxide and carbon monoxide from household items such as heating appliances and volatile organic compounds from building materials. In addition there is carbon dioxide from human breathing and bacteria from speaking, coughing, and sneezing. Objectives: According to recent research results, most indoor air pollution is known to be greatly affected by internal factors such as burning (biomass for cooking) and various pollutants. These pollutants can have a fatal effect on the human body due to a lack of ventilation facilities. Methods: We fabricated a polydopamine (PDA) layer with Ti substrates as a coating on supported glass fiber fabric to enhance its photo-activity. The PDA layer with TiO₂ was covalently attached to glass fiber fabric using the drop-casting method. The roughness and functional groups of the surface of the Ti substrate/PDA coated glass fiber fabric were verified through infrared imaging microscopy and field emission scanning electron microscopy (FE-SEM). The obtained hybrid Ti substrate/PDA coated glass fiber fabric was investigated for photocatalytic activity by the removal of ammonia and an epidermal Staphylococcus aureus reduction test with lamp (250 nm, 405 nm wavelength) at 24℃. Results: Antibacterial properties were found to reduce epidermal staphylococcus aureus in the Ti substrate/PDA coated glass fiber fabric under 405 nm after three hours. In addition, the Ti substrate/PDA coated glass fiber fabric of VOC reduction rate for ammonia was 50% under 405 nm after 30 min. Conclusions: An electron-hole pair due to photoexcitation is generated in the PDA layer and transferred to the conduction band of TiO₂. This generates a superoxide radical that degrades ammonia and removes epidermal Staphylococcus aureus.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.1
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• pp.51-62
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• 2022

Objectives Objective of this study is to investigate the role of paraspinal muscles by examining the correlation between slip percentage (SP) of spondylolisthesis and fatty infiltration of lumbar paraspinal muscle. Methods Retrospective analysis was performed on 45 patients diagnosed with spondylolisthesis based on medical records. Using T2-weighted axial magnetic resonance imaging, cross-sectional areas (CSAs) of psoas major (PM), multifidus (MU) and erector spinae (ES) were calculated and divided by CSA of lower level vertebral body (VB). SP was measured using sagittal T2-weighted images. Correlation of SP with muscle relative cross-sectional area (RCSA) and muscle fatty infiltration by Goutallier classification was respectively analyzed using Spearman correlation. Statistic assessment conducted by Wilcoxon signed rank test and paired t-test using program GraphPad prism 5 (GraphPad Software, Inc., San Diego, CA, USA). Results Spondylolisthesis forward slip percentage by Taillard's method was negatively associated with both side MU RCSAs. No significant correlation was found between PM RCSA, ES RCSA and SP. Forward slippage was significantly correlated with fatty infiltration of lumbar paraspinal muscle measured by Goutallier classification. Conclusions This study is to understand the role of paraspinal muscle affecting spinal instability by investigating correlation between statistical deviation of lumbar muscle characters (RCSA, fatty infiltration of lumbar muscle) and SP. We found that spondylolisthesis SP is positively related to fatty infiltration of lumbar paraspinal muscle. and is negatively associated with both side MU RCSAs.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.91-98
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• 2023

PET is used effectively for biochemical or pathological phenomena, disease diagnosis, prognosis determination after treatment, and treatment planning because it can quantify physiological indicators in the human body by imaging the distribution of various biochemical substances. However, since respiratory motion artifacts may occur due to the movement of the diaphragm due to breathing, we would like to evaluate the practical effect by using the a device-less data-driven gated (DDG) technique called MotionFree with the phase-based gating correction method called Q.static scan mode. In this study, images of changes in moving distance (0 cm, 1 cm, 2 cm, 3 cm) are acquired using a breathing-simulated moving phantom. The diameters of the six spheres in the phantom are 10 mm, 13 mm, 17 mm, 22 mm, 28 mm, and 37 mm, respectively. According to maximum standardized uptake value (SUVmax) measurements, when DDG was applied based on the moving distance, the average SUVmax of the correction effect by the moving distance was improved by 1.92, 2.48, 3.23 and 3.00, respectively. When DDG was applied based on the diameter of the phantom spheres, the average SUVmax of the correction effect by the moving distance was improved by 2.37, 2.02, 1.44, 1.20, 0.42 and 0.52 respectively.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.961-968
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• 2022

Rotator cuff tears are a leading cause of shoulder pain in adults. Due to the increase in social activities, the number of patients complaining of shoulder pain is increasing, and interest in shoulder diseases is also increasing. With the development of ultrasound equipment, the sensitivity and specificity of diagnosis are high, and it is used to diagnose rotator cuff tears in musculoskeletal disease. Ultrasound is recognized as a complementary method to MRI examination in rotator cuff tears. Therefore, this study aimed to find out the diagnostic usefulness of ultrasound and MRI examinations in the diagnosis of shoulder rotator cuff tears.A retrospective analysis was performed on 262 patients who were diagnosed with final rotator cuff damage by arthroscopy after completing ultrasound and MRI examinations. Sensitivity, feature, positive predictive value, image predictive value, and touch were disassembled for the test results. In addition, the degree of clavicular tear was scored and recorded in 5 stages. Ultrasound examination was similar to MRI examination results for both full-thickness and partial tears, and there was no statistically significant difference. Partial tear test results showed higher positive predictive value and accuracy than MRI test. In conclusion, ultrasound can be fully utilized as a screening test for rotator cuff disease, and it is thought that it will be selected and used clinically according to the patient's constitution and situation.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.3
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• pp.138-149
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• 2022

The clinical applicability of ceramics can be increased by analyzing the causes of fractures after fracture testing of dental ceramics. Fractography to analyze the cause of fracture of dental ceramics is being widely applied with the development of imaging technologies such as scanning electron microscopy. Setting the experimental conditions is important for accurate interpretation. The fractured specimens should be stored and cleaned to avoid contamination, and metal pretreatment is required for better observation. Depending on the type of fracture, there are dimple rupture, cleavage, and decohesive rupture mainly observed in metals, and fatigue fractures and conchoidal fractures observed in ceramics. In order to reproduce fatigue fracture in the laboratory, which is the main cause of fracture of ceramics, a dynamic loading for observing slow crack growth is essential, and the load conditions and number of loads must be appropriately set. A typical characteristic of a fracture surface of ceramic is a hackle, and the causes of fracture vary depending on the shape of hackle. Fractography is a useful method for in-depth understanding of fractures of dental ceramics, so it is necessary to follow the exact experimental procedure and interpret the results with caution.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.37-42
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• 2022

Purpose Since 1985, the Korean society of nuclear medicine technology (KSNMT) has been engaged in academic activities related to nuclear medicine imaging. From 2017 to 2021, the papers published in the journal were classified by the specific fields to examine the trends in the research and the direction of nuclear medicine in comparison with the papers submitted to the Korean Society of Nuclear Medicine (KSNM) during the same period. Materials and Methods From 2017 to 2021, papers submitted to KSNMT and KSNM were classified and databaseization using the Excel program by submission type, examination equipment, and examination field. Through this data, the number of papers published in journals by year, the number of papers submitted by detailed fields, and key words by era were analyzed and compared. Results The papers included by journal was 57 KSNMT and 280 KSNM. The major large classification of equipment, PET, Planar and SPECT was 26.3%, 21.1%, 19.3% in the KSNMT, KSNM was 49.6%, 6.4%, and 9.3%, with 66.7% and 65.3%, respectively. the major medium classification of equipment, industrial safety, urogenital system, nervous system, and quality control accounted for 54.4% of the total papers of the total ratio in the KSNMT, while the medium classification of oncology, endocrine system, urogenital system, therapy, and nervous system accounted for 61.1% of KSNM. In the major small classification of image acquisition, improvement effect, and exposure management accounted for 70.2% in KSNMT, while the items of image acquisition, report, and improvement effect accounted for 60.7% in KSNM. The major keywords except for equipment-related keywords such as PET/CT, PET/MR, and SPECT were SUV, Planar Image, and Respiration Gating Method in KSNMT and Ga68, Thyroid, and Lymphoma in the KSNM. Conclusion When checking the last 5 years of submissions, we can see that KSNMT is mainly concerned with image acquisition using existing radiotracers, while KSNM has focused on new radiotracers such as ⁶⁸Ga, ¹⁷⁷Lu, etc., and new medical technologies of theranostic. It has been confirmed that more PET-related papers than other examination equipment will account for a greater number of papers, and it is believed that future submissions will also account for a higher proportion of PET-related papers than other equipment.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.