• Korean Journal of Community Nutrition
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• v.22 no.5
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• pp.413-425
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• 2017

Objectives: Accurate assessment of energy expenditure is important for estimation of energy requirements in athletic children. The objective of this study was to evaluate the accuracy of accelerometer for prediction of selected activities' energy expenditure and intensity in athletic elementary school children. Methods: The present study involved 31 soccer players (16 males and 15 females) from an elementary school (9-12 years). During the measurements, children performed eight selected activities while simultaneously wearing the accelerometer and carrying the portable indirect calorimeter. Five equations (Freedson/Trost, Treuth, Pate, Puyau, Mattocks) were assessed for the prediction of energy expenditure from accelerometer counts, while Evenson equation was added for prediction of activity intensity, making six equations in total. The accuracy of accelerometer for energy prediction was assessed by comparing measured and predicted values, using the paired t-test. The intensity classification accuracy was evaluated with kappa statistics and ROC-Curve. Results: For activities of lying down, television viewing and reading, Freedson/Trost, Treuth were accurate in predicting energy expenditure. Regarding Pate, it was accurate for vacuuming and slow treadmill walking energy prediction. Mattocks was accurate in treadmill running activities. Concerning activity intensity classification accuracy, Pate (kappa=0.72) had the best performance across the four intensities (sedentary, light, moderate, vigorous). In case of the sedentary activities, all equations had a good prediction accuracy, while with light activities and Vigorous activities, Pate had an excellent accuracy (ROC-AUC=0.91, 0.94). For Moderate activities, all equations showed a poor performance. Conclusions: In conclusion, none of the assessed equations was accurate in predicting energy expenditure across all assessed activities in athletic children. For activity intensity classification, Pate had the best prediction accuracy.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.883-889
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• 2021

This study was conducted on 101 patients who visited hospital for abdominal ultrasonography from May 2020 to December 2020. The purpose of this study was to find out the elasticity according to the ultrasound images (echo pattern, splenomegaly, hepatitis) during the ultrasound examination using the shear wave elastography. The shear wave elastography value of the normal group of the echo pattern was 5.75±1.58 kPa, and the group with the abnormal echo pattern was 8.84±4.94 kPa, and the shear wave elastography value of the abnormal group was high (p<0.05). In normal spleen size, hepatic elasticity value was 6.33±2.54 kPa, and hepatic elasticity value of splenomegaly was 13.73±5.48 kPa. In the case of splenomegaly, the liver elasticity value was high, and there was a statistically significant difference (p<0.05). As the spleen size increased, the liver elasticity value increased by 1.485 times, and as hepatitis progressed, the liver elasticity value increased by 1.573 times (p<0.05). As a result of analysis of concordance between ultrasound imaging findings and shear wave elastography, the Kappa value was found to be as high as 0.922 (p<0.05), which showed high concordance between the two test methods. Additional comparisons of liver elasticity values in shearwave elastography tests along with liver ultrasound findings are thought to be of great help in diagnosing liver fibrosis.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.390-400
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• 2022

University Identity can effectively convey the core values for which schools aim by establishing university identity and integrating one unique image. Therefore, most universities are actively implementing promotional strategies such as newly defining university identity or releasing cultural products. Recently, university brands have been continuously exposed and differentiated through SNS such as Instagram, YouTube, and Facebook as well as existing advertisements and homepages. This study analyzes the identities of the top 80 universities in Korea and China, by referring to the rankings of Asian universities in the 2021 QS World University Rankings, and addresses differences in terms of design shape, number of colors, and use of English. Moreover, 'Cohen's Kappa' consistency analysis was applied to secure data accuracy by analyzing the difference in visual expression of university identity between the two countries through quantification and cross-analysis of visualized university identity design of Korean and Chinese universities. As a result of the study, it is creative, irregular, and has a lot of use of blue, red, and green, and most of them can be seen in less than two colors. In addition, it turns out that word marks and abstract forms of expression are used for university identity design. This study can present implications as effective basic data for internationalizing universities and creating differentiated university identity designs in the future.

• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.203-211
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• 2022

PURPOSE. The aim of this study was to introduce rating criteria to evaluate student performance in a newly developed, digital wax-up preclinical program for computer-aided design (CAD) of full-coverage crowns and preliminarily investigate the reliability and internal consistency of the rating system. MATERIALS AND METHODS. This study, conducted in 2017, enrolled 47 fifth-year dental students of Okayama University Dental School. Digital wax-up training included a fundamental practice using computer graphics (CG), multipurpose CAD software programs, and an advanced practice to execute a digital wax-up of the right mandibular second molar (#47). Each student's digital wax-up work (stereolithography data) was evaluated by two instructors using seven qualitative criteria. The total qualitative score (0-90) of the criteria was calculated. The total volumetric discrepancy between each student's digital wax-up work and a reference prepared by an instructor was automatically measured by the CAD software. The inter-rater reliability of each criterion was analyzed using a weighted kappa index. The relationship between the total volume discrepancy and the total qualitative score was analyzed using Spearman's correlation. RESULTS. The weighted kappa values for the seven qualitative criteria ranged from 0.62 - 0.93. The total qualitative score and the total volumetric discrepancy were negatively correlated (ρ = -0.27, P = .09, respectively); however, this was not statistically significant. CONCLUSION. The established qualitative criteria to evaluate students' work showed sufficiently high inter-rater reliability; however, the digitally measured volumetric discrepancy could not sufficiently predict the total qualitative score.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.291-298
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• 2022

Recently developed drones are inexpensive and very convenient to operate. As a result, the production and utilization of spatial information using drones are increasing. However, most drones acquire images with a low-cost global navigation satellite system (GNSS) and an inertial measurement unit (IMU). Accordingly, the accuracy of the initial location and rotation angle elements of the image is low. In addition, because these drones are small and light, they can be greatly affected by wind, making it difficult to maintain a certain overlap, which degrades the positioning accuracy. Therefore, in this study, images are taken at different times in order to analyze the positioning accuracy according to changes in certain exterior orientation parameters. To do this, image processing was performed with Pix4D Mapper and the accuracy of the results was analyzed. In order to analyze the variation of the accuracy according to the exterior orientation parameters in detail, the exterior orientation parameters of the first processing result were used as meta-data for the second processing. Subsequently, the amount of change in the exterior orientation parameters was analyzed by in a strip-by-strip manner. As a result, it was proved that the changes of the Omega and Phi values among the rotation elements were related to a decrease in the height accuracy, while changes in Kappa were linked to the horizontal accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.351-362
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• 2024

Objective: To measure inter-reader agreement and identify associated factors in interpreting complete response (CR) on magnetic resonance imaging (MRI) following chemoradiotherapy (CRT) for rectal cancer. Materials and Methods: This retrospective study involved 10 readers from seven hospitals with experience of 80-10210 cases, and 149 patients who underwent surgery after CRT for rectal cancer. Using MRI-based tumor regression grading (mrTRG) and methods employed in daily practice, the readers independently assessed mrTRG, CR on T2-weighted images (T2WI) denoted as mrCR_T2W, and CR on all images including diffusion-weighted images (DWI) denoted as mrCR_overall. The readers described their interpretation patterns and how they utilized DWI. Inter-reader agreement was measured using multi-rater kappa, and associated factors were analyzed using multivariable regression. Correlation between sensitivity and specificity of each reader was analyzed using Spearman coefficient. Results: The mrCR_T2W and mrCR_overall rates varied widely among the readers, ranging 18.8%-40.3% and 18.1%-34.9%, respectively. Nine readers used DWI as a supplement sequence, which modified interpretations on T2WI in 2.7% of cases (36/1341 [149 patients × 9 readers]) and mostly (33/36) changed mrCR_T2W to non-mrCR_overall. The kappa values for mrTRG, mrCR_T2W, and mrCR_overall were 0.56 (95% confidence interval: 0.49, 0.62), 0.55 (0.52, 0.57), and 0.54 (0.51, 0.57), respectively. No use of rectal gel, larger initial tumor size, and higher initial cT stage exhibited significant association with a higher interreader agreement for assessing mrCR_overall (P ≤ 0.042). Strong negative correlations were observed between the sensitivity and specificity of individual readers (coefficient, -0.718 to -0.963; P ≤ 0.019). Conclusion: Inter-reader agreement was moderate for assessing CR on post-CRT MRI. Readers' varying standards on MRI interpretation (i.e., threshold effect), along with the use of rectal gel, initial tumor size, and initial cT stage, were significant factors associated with inter-reader agreement.

• Korean Journal of Radiology
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• v.25 no.6
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• pp.575-588
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• 2024

Objective: Differentiating intracranial aneurysms from normal variants using CT angiography (CTA) or MR angiography (MRA) poses significant challenges. This study aimed to evaluate the efficacy of proton-density MRA (PD-MRA) compared to high-resolution time-of-flight MRA (HR-MRA) in diagnosing aneurysms among patients with indeterminate findings on conventional CTA or MRA. Materials and Methods: In this retrospective analysis, we included patients who underwent both PD-MRA and HR-MRA from August 2020 to July 2022 to assess lesions deemed indeterminate on prior conventional CTA or MRA examinations. Three experienced neuroradiologists independently reviewed the lesions using HR-MRA and PD-MRA with reconstructed voxel sizes of 0.25³ mm³ or 0.2³ mm³, respectively. A neurointerventionist established the gold standard with digital subtraction angiography. We compared the performance of HR-MRA, PD-MRA (0.25³-mm³ voxel), and PD-MRA (0.2³-mm³ voxel) in diagnosing aneurysms, both per lesion and per patient. The Fleiss kappa statistic was used to calculate inter-reader agreement. Results: The study involved 109 patients (average age 57.4 ± 11.0 years; male:female ratio, 11:98) with 141 indeterminate lesions. Of these, 78 lesions (55.3%) in 69 patients were confirmed as aneurysms by the reference standard. PD-MRA (0.25³-mm³ voxel) exhibited significantly higher per-lesion diagnostic performance compared to HR-MRA across all three readers: sensitivity ranged from 87.2%-91.0% versus 66.7%-70.5%; specificity from 93.7%-96.8% versus 58.7%-68.3%; and accuracy from 90.8%-92.9% versus 63.8%-69.5% (P ≤ 0.003). Furthermore, PD-MRA (0.25³-mm³ voxel) demonstrated significantly superior per-patient specificity and accuracy compared to HR-MRA across all evaluators (P ≤ 0.013). The diagnostic accuracy of PD-MRA (0.2³-mm³ voxel) surpassed that of HR-MRA and was comparable to PD-MRA (0.25³-mm³ voxel). The kappa values for inter-reader agreements were significantly higher in PD-MRA (0.820-0.938) than in HR-MRA (0.447-0.510). Conclusion: PD-MRA outperformed HR-MRA in diagnostic accuracy and demonstrated almost perfect inter-reader consistency in identifying intracranial aneurysms among patients with lesions initially indeterminate on CTA or MRA.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1218-1230
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• 2021

Purpose To compare the per-patient diagnostic performance of simulated abbreviated MRI (AMRI) to that of conventional MRI (CMRI) with full-sequence dynamic gadoxetic acid (GA) enhancement for early-stage hepatocellular carcinoma (HCC) screening in high-risk patients. Materials and Methods A total of 201 consecutive patients at high-risk for HCC, who underwent 3T liver MRI, were included in this retrospective study. The AMRI protocol comprised T2-weighted imaging, hepatobiliary phase imaging after GA injection, and diffusion-weighted imaging. For each patient, two AMRI and CMRI image sets were independently reviewed by two radiologists. Inter-reader agreement was assessed using Cohen's kappa value. A composite reference standard was used to determine the diagnostic performance of each image set for each reader. Results A total of 93 HCCs were detected in 79 patients. The inter-reader agreement was almost perfect for both image sets (κ = 0.839, 0.948). In AMRI, the per-patient sensitivity and negative predictive values (NPV) were 94.9% and 96.4%, respectively. In CMRI, the per-patient sensitivity and NPV were 96.2% and 97.5%, respectively. Conclusion AMRI, using only three sequences, had a comparable diagnostic performance to CMRI in screening early-stage HCC. AMRI could be an alternative HCC screening tool for high-risk HCC patients.