• Journal of Technologic Dentistry
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• v.38 no.2
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• pp.57-62
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• 2016

Purpose: The purpose of this study was to assess precision of crown prostheses that were fabricated by using 2 kinds of bur or 3 kinds of bur. Methods: The crowns were fabricated by using the first molar of the right maxillary in this study. The abutments that were prepared were scanned by using a scanner and designed by using CAD software. Based on the crown design, NC data were created with CAM software. The created NC data were used while fabricating the crown prostheses by using 5-axis milling machine. Scanning was done for the internal and external surface of the completed crown prostheses and 3-dimensional measurement was conducted for precision assessment. Results: The $RMS{\pm}SD$ value for the external surface of the crown prostheses that was fabricated by using two burs and three burs were $28.5{\pm}4.1{\mu}m$ and $19.1{\pm}2.8{\mu}m$, respectively; and the value for two burs were bigger than that for three burs with statistical significance (p<0.001). The $RMS{\pm}SD$ value for the internal surface of the crown prostheses that was fabricated by using two burs and three burs were $14.9{\pm}1.9{\mu}m$ and $13.3{\pm}2.5{\mu}m$, respectively; and the value for two burs were bigger than that for three burs but with no statistical significance. Conclusion: Based on this study, the prostheses that were fabricated by using 3 bur presented better stability compared to those that were fabricated by using 2 bur and statistically significant difference was found only in the external surface.

• Journal of dental hygiene science
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• v.16 no.1
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• pp.18-25
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• 2016

The purpose of this study was to investigate a reliability and validity of the Korean version for measuring tool the job embeddedness of dental hygienists. The survey was modified and revised to fit into Korean culture. A survey was conducted with 274 dental hygienists in dental clinics. The data was used for the analysis of the study, using PASW Statistics 18.0 and IBM SPSS AMOS 7.0. The factor analysis showed that the job embeddedness of the dental hygienists was composed of three elements, namely 'organization fit', 'job connectivity', and 'personnel relationships'. The validity of the model examined by a confirmatory factor analysis satisfied most of the relevant requirements. All of the factors had the conceptual reliability and variant extracted index above the minimum requirements, ensuring reliability and concentrated validity. The Cronbach's alpha shows a good reliability. In conclusion, it was proven that dental hygienist's job embeddedness measurement tool has high validity and reliability. Further, this study could be used to improve dental hygienist's long term working, and the growth stage of dental clinic.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.565-573
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• 2019

This study was conducted on 236 dental hygienists to find ways to improve patient education by identifying their concern in preventive care, their level of prevention management knowledge, and their impact on patient education by mediating their prevention-related knowledge. It was found that the prevention care concern, prevention knowledge and patient education of dental hygienists have a significant correlation, the higher the prevention care concern, the better the patient education, the better the prevention care knowledge, the better the prevention management knowledge, the better the patient education. The regression analysis and Sobel verification were conducted in order to identify a mediating effect of the preventive care knowledge on the influence that the level of concern on preventive dental care affects the patient education. Therefore, it was confirmed that the level of concern on preventive dental care has not only a direct effect and an indirect effect. Based on the results of this study, it is believed that the preventive education effect of patients will be even higher if institutional support is supported along with the attention of dental hygienists on preventive work and education for preventive knowledge content.

• Restorative Dentistry and Endodontics
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• v.32 no.4
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• pp.313-326
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• 2007

The purpose of this study was to prove that an intermediate resin layer (IRL) oan increase the bond strength to dentin by reducing the permeability of single-step adhesives. Flat dentin surfaces were created on buccal and lingual side of freshly extracted third molar using a low-speed diamond saw under copious water flow. Approximately 2.0 mm thick axially sectioned dentin slice was abraded with wet #600 SiC paper. Three single-step self-etch adhesives; Adper Prompt L-Pop (3M ESPE, St Paul, MN, USA), One-Up Bond F (Tokuyama Corp, Tokyo, Japan) and Xeno III (Dentsply, Konstanz, Germany) were used in this study. Each adhesive groups were again subdivided into ten groups by; whether IRL was used or not; whether adhesives were cured with light before application or IRL or not; the mode of composite application. The results of this study were as follows; 1. Bond strength of single-step adhesives increased by an additional coating of intermediate resin layer, and this increasement was statistically signigicant when self-cured composite was used (p < 0.001). 2. When using IRL, there were no difference on bond strengths regardless the curing procedure of single-step adhesives. 3. There were no significant difference on bond strengths between usage of AB2 or SM as an IRL. 4. The thickness of Hybrid layer was correlated with the acidity of adhesive used, and the nanoleakage represented by silver deposits and grains was examined within hybrid and adhesive layer in most of single-step adhesives. 5. Neither thickness of hybrid layer nor nanoleakage were related to bond strength.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.66-71
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• 2005

Soil loss into stream and river by runoff shall be considered for non-point source pollution management as national land conservation. The purpose of this study was to develop the mathematical equation to predict soil loss from inclined uplands of maize cultivation due to the runoff by rainfall which mainly converges on July and August. Soil loss was concentrated on May because of low canopy over an entire field in 2002 and on June and July because of heavy rainfall in 2003. By regression analysis the relation between runoff and soil loss can be represented by a linear equation of y =1.5291x - 3.4933, where y is runoff ($Mg\;ha^{-1}$) and x is soil loss ($kg\;ha^{-1}$). The determination coefficient of this equation was 0.839 (P<0.001). Therefore, the mathematical equation derived from the practical experiment at the inclined upland can be applicable to predict soil loss accompanied by runoff due to periodic rainfall converging on short periods within a couple of months.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1706-1717
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• 2020

The purpose of this study is to understand the effects of nursing ethics education on ethical values, nursing professional intuition and biomedical ethics of nursing students. Data collection period was from December 2, 2017 to December 31, 2017 and the data were collected from 186 nursing college students at 2 universities. Data were analyzed by t-tests, one-way ANOVA, Pearson's correlation coefficient analysis using the SPSS 21.0 program. The average score for the ethics values was 3.08±0.33 points. the average score of nursing professionalism was 3.22±0.84. The average score of consciousness of biomedical ethics was 2.86±0.22. It is necessary to develop case-centered textbooks in order to raise the competence to apply ethics management about biomedical issues in the course program of nursing ethics to improve the awareness of biomedical ethics for nursing college students.

• Korean Journal of Radiology
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• v.22 no.2
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.284-293
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• 2023

Objective: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT). Materials and Methods: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods. Results: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) (P < 0.001). Conclusion: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.720-731
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• 2022

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LV_BSA, and aLSSR × LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LV_BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.