• Korean Journal of Soil Science and Fertilizer
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• v.34 no.5
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• pp.342-349
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• 2001

This study was carried out to classify and evaluate the land cover map using Landsat TM data in Pyeongtaeg City. DGPS data, aerial photography, topographical map were used for selection the training sets and accuracy assessment. The overall accuracy and Kappa coefficient of the land cover classification map(using supervised classification with 13 classes) with Landsat TM data(16 June. 1997) were respectively, 86.8%, 85.4%, but the user's accuracy of urban/village and vinyl-house was below 60%, and the producer's accuracy of read and vinyl-house below 70%. Maybe it was caused the spectral reflectance characteristics, heterogeneity and small distribution area on the artificial things such as urban/village, vinyl_house and road, etc. And then, the agricultural land cover classification system using remote sensing data in Korea was to classify level I and II. Level I consisted of 5 classes such as agricultural land, forest land, water, barren land, urban and built-up land.

• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.342-355
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• 1997

This research was carried out to determine the performance rate of health related practices, to measure the agreement between morbidity by doctor's diagnosis and morbidity by subject' self-reported and the degree of association between health related practices and morbidity rate by doctor's diagnosis, to identify their effects on morbidity among rural area populations. The data were gathered by volunteer residents (over the age of 20) of Haman Myeon, Haman Gun, Kyeongsangnam Do in Korea, from June 10, 1993 to June 12, 1993 (369 male and 516 female). Face to face interview, lab, chest P-A, EKG and physical examination were completed. Descriptive statistics, agreement analysis and multiple logistic regression procedures were employed for analyses. The results of the study were summarized as follows : 1) Age adjusted morbidity rates by doctor's diagnosis and self-reported were 38.5% (male:37.3%, female:36.5%), 26.4% (male:33.3%, female:27.5%), respectively. Kappa coefficient between morbidity by doctor's diagnosis and morbidity by self-reported was 0.21 (male:0.21, female:0.22). 2) The frequency of disease by doctor's diagnosis was as follows: hypertension(15.3%), gastritis (9.6%), diabetes mellitus (8.5%), live. disease (8.1%), and degenerative arthritis (6.2%) in the study population. 3) Order of health practice performance rate was as follows: Males-normal body weight (62.1%), non-heavy alcohol consumption (57.5%), 7-8 hours of sleeping (50.1%), non-smoking (21.7%), and exercise (19.8%). Females- non-heavy alcohol consumption (97.3%), non-smoking (84.7%), normal body weight (57.8%), 7-8 hours of sleeping (45.0%), and exercise (9.9%). 4) There was no significant relationship between health related practice and morbidity except exercise among health related practices. 5) Health related practice index which was recategorized by high, medium, and low had effects on the probability of developing morbidity.

• Clinical and Experimental Reproductive Medicine
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• v.24 no.2
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• pp.153-165
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• 1997

In male reproducible health, fertility and IVF (in-vitro fertilization), semen analysis has been most important. Semen analysis can be divided into concentration, motional and morphological analysis of sperm. The existing method which was developed earlier to analyze semen concentrated on the sperm motility analysis. To provide more useful and precise solutions for clinical problems such as infertility, semen analysis must include sperm morphological analysis. But the traditional tools for semen analysis are subjective, imprecise, inaccurate, difficult to standardize, and difficult to reproduce. Therefore, with the help of development of microcomputers and image processing techniques, we developed a new sperm morphology analyzer to overcome these problems. In this study the agreement on percent normal morphology was studied between different observers and a computerized sperm morphology analyzer on a slide-by-slide basis using strict criteria. Slides from 30 different patients from the SNUH andrology laboratory were selected randomly. Microscopic fields and sperm cells were chosen randomly and percent normal morphology was recorded. The ability of sperm morphology analyzer to repeat the same reading for normal and abnormal cells was studied. The results showed that there was no significant bias between two experienced observers. The limits of agreement were 4.1%${\sim}$-3.8%. The Pearson correlation coefficient between readers was 0.79. Between the manual and sperm morphology analyzer, the same findings were reported. In this experiments the slides were stained by two different methods, PAP and Diff-Quik staining methods. The limits of agreement were 7.2%${\sim}$-5.7% and 6.0%${\sim}$-6.3%, respectively. The Pearson correlation coefficients ware 0.76 and 0.91, respectively. The limits of agreement was tighter below 20% normal forms. In the experiments of repeatability, 52 cells stained by PAP and Diff-Quik staining methods were analyzed three times in succession. Estimating pairwise agreement, the kappa statistic for the pairs were 0.76, 0.81, 0.86, and 0.75, 0.88, 0.88 respectively. In this study it was shown that there was good agreement between manual and computerized assessment of normal and abnormal cells. The repeatability and agreement per slide of computerized sperm morphology analyzer was excellent. The computer's ability to classify normal morphology per slide is promising. Based on results obtained, this system can be of clinical value both in andrology laboratories and IVF units.

• Korean Journal of Community Nutrition
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• v.26 no.4
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• pp.280-295
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• 2021

Objectives: This study aimed to examine the characteristics of patients according to their nutritional status as assessed by five nutritional screening tools: Patient-Generated Subjective Global Assessment (PG-SGA), NUTRISCORE, Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) and to compare the agreement, sensitivity, and specificity of these tools. Methods: A total of 952 gastric cancer patients who underwent gastrectomy and chemotherapy from January 2009 to December 2012 at the Samsung Medical Center were included. We categorized patients into malnourished and normal according to the five nutritional screening tools 1 month after surgery and compared their characteristics. We also calculated the Spearman partial correlation, Cohen's Kappa coefficient, the area under the curve (AUC), sensitivity, and specificity of each pair of screening tools. Results: We observed 86.24% malnutrition based on the PG-SGA and 85.82% based on the NUTRISCORE among gastric cancer patients in our study. When we applied NRI or CONUT, however, the malnutrition levels were less than 30%. Patients with malnutrition as assessed by the PG-SGA, NUTRISCORE, or NRI had lower intakes of energy and protein compared to normal patients. When NRI, PNI, or CONUT were used to identify malnutrition, lower levels of albumin, hemoglobin, total lymphocyte count, total cholesterol, and longer postoperative hospital stays were observed among patients with malnutrition compared to those without malnutrition. We found relatively high agreement between PG-SGA and NUTRISCORE; sensitivity was 90.86% and AUC was 0.78. When we compared NRI and PNI, sensitivity was 99.64% and AUC was 0.97. AUC ranged from 0.50 to 0.67 for comparisons between CONUT and each of the other nutritional screening tools. Conclusions: Our study suggests that PG-SGA and NRI have a relatively high agreement with the NUTRISCORE and PNI, respectively. Further cohort studies are needed to examine whether the nutritional status assessed by PG-SGA, NUTRISCORE, NRI, PNI, and CONUT predicts the gastric cancer prognosis.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Korean Journal of Community Nutrition
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• v.27 no.3
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• pp.205-222
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• 2022

Objectives: This study examined the characteristics of patients according to nutritional status assessed by five nutritional screening tools: Patient-Generated Subjective Global Assessment (PG-SGA), NUTRISCORE, Nutritional Risk Index (NRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) and to compare the agreement, sensitivity, and specificity of these tools. Methods: A total of 952 gastric cancer patients who underwent gastrectomy and chemotherapy from January 2009 to December 2012 were included. The patients were categorized into malnutrition and normal status according to five nutritional screening tools one month after surgery. The Spearman partial correlation, Cohen's Kappa coefficient, the area under the curve (AUC), sensitivity, and specificity of each two screening tools were calculated. Results: Malnutrition was observed in 86.24% of patients based on the PG-SGA and 85.82% based on the NUTRISCORE. When NRI or CONUT were applied, the proportions of malnutrition were < 30%. Patients with malnutrition had lower intakes of energy and protein than normal patients when assessed using the PG-SGA, NUTRISCORE, or NRI. Lower levels of albumin, hemoglobin, total lymphocyte count, and total cholesterol and longer postoperative hospital stays were observed among patients with malnutrition compared to normal patients when NRI, PNI, or CONUT were applied. Relatively high agreement for NUTRISCORE relative to PG-SGA was found; the sensitivity was 90.86%, and the AUC was 0.78. When NRI, PNI, and CONUT were compared, the sensitivities were 23.72% for PNI relative to NRI, 44.53% for CONUT relative to NRI, and 90.91% for CONUT relative to PNI. The AUCs were 0.95 for NRI relative to PNI and 0.91 for CONUT relative to PNI. Conclusions: NUTRISCORE had a high sensitivity compared to PG-SGA, and CONUT had a high sensitivity compared to PNI. NRI had a high specificity compared to PNI. This relatively high sensitivity and specificity resulted in 77.00% agreement between PNI and CONUT and 77.94% agreement between NRI and PNI. Further cohort studies will be needed to determine if the nutritional status assessed by PG-SGA, NUTRISCORE, NRI, PNI, and CONUT predicts the gastric cancer prognosis.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Sim-seong Yeon-gu
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• v.33 no.1
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• pp.61-80
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• 2018

We investigated whether the 81 items of the Gray-Wheelwright test correctly measure the concept of Jung's typology and aimed to refine the test. Participants (n=431) completed the Gray-Wheelwright test, and the results were analyzed using factor analysis with the varimax rotation and the maximum likelihood extraction method. A pair of opposing attitudes, introversion/extroversion, or one of the two pairs of opposing functional types, thinking/feeling or intuition/sensation, was labeled to the extracted factor according to the majority type of the items in the factor. The minority items or items not included in any factors were excluded from making a short form of the Gray-Wheelwright test with 45 items. We used intraclass correlation (ICC) coefficient and Cronbach's alpha for the test-retest reliability and internal consistency of the test, respectively. The newly developed short form of the Gray-Wheelwright test measured the Jung's personality types well, which was comparable to the original one while reducing time and effort required for the testing.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.267-276
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• 2024

Objective: To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. Materials and Methods: This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and ¹⁸F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. ¹⁸F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen;s kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. Results: In total, 194 patients were included, of whom 105 and 103 had positive results on ¹⁸F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05). Conclusion: There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.641-648
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• 2019

Objective: The purpose of this study was to investigate whether three-dimensional (3D) magnetic resonance imaging could improve diagnostic accuracy for suspected posterior ligamentous complex (PLC) disruption. Materials and Methods: We used 20 freshly harvested goat spine samples with 60 segments and intact surrounding soft tissue. The animals were aged 1-1.5 years and consisted of 8 males and 12 females, which were sexually mature but had not reached adult weights. We created a paraspinal contusion model by percutaneously injecting 10 mL saline into each side of the interspinous ligament (ISL). All segments underwent T2-weighted sagittal and coronal short inversion time inversion recovery (STIR) scans as well as coronal and sagittal 3D proton density-weighted spectrally selective inversion recovery (3D-PDW-SPIR) scans acquired at 1.5T. Following scanning, some ISLs were cut and then the segments were rescanned using the same magnetic resonance (MR) techniques. Two radiologists independently assessed the MR images, and the reliability of ISL tear interpretation was assessed using the kappa coefficient. The chi-square test was used to compare the diagnostic accuracy of images obtained using the different MR techniques. Results: The interobserver reliability for detecting ISL disruption was high for all imaging techniques (0.776-0.949). The sensitivity, specificity, and diagnostic accuracy of the coronal 3D-PDW-SPIR technique for detecting ISL tears were 100, 96.9, and 97.9%, respectively, which were significantly higher than those of the sagittal STIR (p = 0.000), coronal STIR (p = 0.000), and sagittal 3D-PDW-SPIR (p = 0.001) techniques. Conclusion: Compared to other MR methods, coronal 3D-PDW-SPIR provides a more accurate diagnosis of ISL disruption. Adding coronal 3D-PDW-SPIR to a routine MR protocol may help to identify PLC disruptions in cases with nearby contusion.