• Journal of radiological science and technology
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• v.44 no.4
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• pp.307-313
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• 2021

The purpose of this study was to analyze the risk factors for uterine myoma diagnosed by ultrasonography in Korea women and to evaluate the risk. Among the patients who visited the outpatient department of obstetrics and gynecology at I hospital in Busin between January 2019 and March 2021 for the purpose of examination, 98 patients in the experimental group diagnosed with uterine myoma and 163 patients in the normal control group without other diseases were retrospectively conducted. Among the general characteristics of the subjects, age, body mass index, parity, and menopause showed significant differences between the myoma group and the normal control group. ROC(receiver operating characteristic) curve analysis and logistic regression analysis were performed to obtain the cut off value and odds ratio that can predict the occurrence of uterine myoma. The cut off value for the prediction of uterine myoma was determined to be 30 years old and a body mass index of 23 kg/m². After that adjusting for menopause, non menopausal cases with a body mass index of 23 kg/m² and over 39 years of age had the highest odds ratio of 6.04. Therefore, premenopausal women over 40 years of age require regular checkups and thorough weight management. This study was conducted with a small number of subjects. Therefore, there is a limit to generalizing to all Korean women. However, based on this study if a large scale prospective study considering various variables is made, it can play a role as a predictive marker in early detection of uterine myoma.

• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.189-197
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• 2021

Objective : This study aims to investigate the relationship between aneurysm wall enhancement and clinical rupture risks based on the magnetic resonance vessel wall imaging (MR-VWI) quantitative methods. Methods : One hundred and eight patients with 127 unruptured aneurysms were prospectively enrolled from Feburary 2016 to October 2017. Aneurysms were divided into high risk (≥10) and intermediate-low risk group (<10) according to the PHASES (Population, Hypertension, Age, Size of aneurysm, Earlier SAH history from another aneurysm, Site of aneurysm) scores. Clinical risk factors, aneurysm morphology, and wall enhancement index (WEI) calculated using 3D MR-VWI were analyzed and compared. Results : In comparison of high-risk and intermediated-low risk groups, univariate analysis showed that neck width (4.5±3.3 mm vs. 3.4±1.7 mm, p=0.002), the presence of wall enhancement (100.0% vs. 62.9%, p<0.001), and WEI (1.6±0.6 vs. 0.8±0.8, p<0.001) were significantly associated with high rupture risk. Multivariate regression analysis revealed that WEI was the most important factor in predicting high rupture risk (odds ratio, 2.6; 95% confidence interval, 1.4-4.9; p=0.002). The receiver operating characteristic (ROC) curve analysis can efficiently differentiate higher risk aneurysms (area under the curve, 0.780; p<0.001) which have a reliable WEI cutoff value (1.04; sensitivity, 0.833; specificity, 0.67) predictive of high rupture risk. Conclusion : Aneurysms with higher rupture risk based on PHASES score demonstrate increased neck width, wall enhancement, and the enhancement intensity. Higher WEI in unruptured aneurysms has a predictive value for increased rupture risk.

• Journal of dental hygiene science
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• v.20 no.4
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• Imaging Science in Dentistry
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• v.51 no.1
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• pp.55-62
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• 2021

Purpose: This study evaluated the influence of a metal artifact reduction (MAR) tool in a cone-beam computed tomography (CBCT) device on the diagnosis of vertical root fractures (VRFs) in teeth with different root filling materials. Materials and Methods: Forty-five extracted human premolars were classified into three subgroups; 1) no filling; 2) gutta-percha; and 3) metallic post. CBCT images were acquired using an Orthopantomograph 300 unit with and without a MAR tool. Subsequently, the same teeth were fractured, and new CBCT scans were obtained with and without MAR. Two oral radiologists evaluated the images regarding the presence or absence of VRF. Receiver operating characteristic (ROC) curves and diagnostic tests were performed. Results: The overall area under the curve values were 0.695 for CBCT with MAR and 0.789 for CBCT without MAR. The MAR tool negatively influenced the overall diagnosis of VRFs in all tested subgroups, with lower accuracy (0.45-0.72), sensitivity (0.6-0.67), and specificity (0.23-0.8) than were found for the images without MAR. In the latter group, the accuracy, sensitivity, and specificity values were 0.68-0.77, 0.67-083, and 0.53-087, respectively. However, no significant difference was found between images with and without MAR for the no filling and gutta-percha subgroups (P>0.05). In the metallic post subgroup, CBCT showed a significant difference according to MAR use (P<0.05). Conclusion: The OP 300 MAR tool negatively influenced the detection of VRFs in teeth with no root canal filling, gutta-percha, or metallic posts. Teeth with metallic posts suffered the most from the negative impact of MAR.

• Food Science of Animal Resources
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• v.41 no.2
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• pp.312-323
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• 2021

The purpose of this study was to use 1H nuclear magnetic resonance (1H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using 1H-13C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R2=0.945; Q2=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both 1H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.

• Journal of Environmental Health Sciences
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• v.47 no.4
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• pp.320-329
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• 2021

Background: Smoking exposure may be objectively assessed through specific biomarkers. The most common biomarker for smoking is cotinine concentration in urine, and setting an optimal cut-off point can accurately classify smoking status. Such a cut-off point for Korean adolescents has never been studied. Objectives: The aim of this study was to determine a cut-off point for urinary cotinine concentration for the discrimination of smoking in adolescents. Methods: Participants were adolescents aged 13~18 years who participated in the third cycle of the Korean National Environmental Health Survey. We used urine samples to confirm the level of cotinine concentrations. Smoking status was determined by self-reported questionnaire. We identified the optimal cotinine cut-off point for discriminating smoking status using receiver operating characteristic curve analysis. Results: Of the 904 participants, 28 (3.1%) were smokers, among whom 20 (71.4%) were male. The median urinary cotinine concentrations in smokers was 218 ㎍/L (male: 215 ㎍/L, female: 303 ㎍/L), and that in non-smokers was 1.31 ㎍/L (male: 1.46 ㎍/L, female: 1.18 ㎍/L). We found significant differences in urinary cotinine concentration according to smoking status and sex (p<0.001). Urinary cotinine concentrations performed well for identifying smoking adolescents [area under the curve: 0.954 (male: 0.963, female: 0.908)]. The cut-off that optimally distinguished smokers from non-smokers was 39.85 ㎍/L (sensitivity: 89.3%, specificity: 97.4%). Male [39.85 ㎍/L (sensitivity: 90.0%, specificity: 94.9%)] had a different optimal cut-off point than female [26.26 ㎍/L (sensitivity: 87.5%, specificity: 99.6%)]. Conclusions: This study determined a cut-off point for urinary cotinine of 39.85 ㎍/L (male: 39.85 ㎍/L, female: 26.26 ㎍/L) to distinguish smokers from non-smokers in adolescents.

• Journal of East-West Nursing Research
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• v.28 no.1
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• pp.10-20
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• 2022

Purpose: We compared the Korean Triage and Acuity Scale (KTAS), Injury Severity Score (ISS), and Revised Trauma Score (RTS) determined the validity of KTAS for classifying trauma patients. Methods: A retrospective chart review of 10,865 trauma patients (aged ≥15 years) who visited a single regional trauma and emergency medical center from January 1, 2016, to December 31, 2020, was conducted. Data were collected from the Korean Trauma Data Bank. Based on KTAS classification, the rates of intensive care unit admission, surgery and intervention, transfusion, emergency room (ER) and hospital mortality, and ER stay time were investigated. Data were analyzed using Chi-square test, Pearson's correlation coefficient, receiver operating characteristic curve, and area under the ROC curve. Results: In the KTAS, severe trauma patients (ISS ≥16) were classified as Level 1 (79.6%), 2 (44.8%), 3 (15.5%), 4 (4.0%) and 5 (7.6%). The following were the predictive powers of KTAS, ISS, and RTS for different parameters: surgery and intervention rate, KTAS (.71), ISS (.70), and RTS (.63); transfusion rate within 4h, KTAS (.82), ISS (.82), and RTS (.74); ER stay time within 90 min, KTAS (.72), ISS (.62), and RTS (.56); and ER mortality, KTAS (.84), ISS (.72), and RTS (.88). These findings were statistically significant (p<.001). The sensitivity and specificity of KTAS for trauma patients were .88 (.87~.90), and .38 (.37~.39), respectively. Conclusion: KTAS is a useful classification system that can predict the clinical outcomes of patients with trauma, and effectively triage acutely ill trauma patients, thus provide appropriate treatment.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.323-331
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• 2021

Purpose: To investigate the diagnostic criteria of T1-weighted imaging (T1W) and time-of-flight (TOF) imaging for detecting intraplaque hemorrhage (IPH) of a vertebrobasilar artery (VBA) compared with simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) imaging. Materials and Methods: Eighty-seven patients with VBA atherosclerosis who underwent high resolution MR imaging for evaluation of VBA plaque were reviewed. The presence and location of VBA plaque and IPH on SNAP were determined. The signal intensity (SI) of the VBA plaque on T1W and TOF imaging was manually measured and the SI ratio against adjacent muscles was calculated. The receiver-operating characteristic (ROC) curve was used to compare the diagnostic accuracy for detecting VBA IPH. Results: Of 87 patients, 67 had IPH and 20 had no IPH on SNAP. The SI ratio between VBA IPH and temporalis muscle on T1W was significantly higher than that in the no-IPH group (235.9 ± 16.8 vs. 120.0 ± 5.1, P < 0.001). The SI ratio between IPH and temporalis muscle on TOF was also significantly higher than that in the no-IPH group (236.8 ± 13.3 vs. 112.8 ± 7.4, P < 0.001). Diagnostic efficacies of SI ratios on TOF and TIW were excellent (AUC: 0.976 on TOF and 0.964 on T1W; cutoff value: 136.7% for TOF imaging and 135.1% for T1W imaging). Conclusion: Compared with SNAP, cutoff levels of the SI ratio between VBA plaque and temporalis muscle on T1W and TOF imaging for detecting IPH were approximately 1.35 times.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.53-53
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• 2023

본 연구에서는 토양의 동결-융해 상태 구분을 위해 영상레이더(Synthetic Aperture Radar) 자료를 활용해 지표상태인자(Surface State Factor, SSF)를 산정하고, 관측 토양수분 자료 및 지표면 온도(Land Surface Temperature, LST) 자료와의 비교를 통해 SSF의 정확도를 분석하였다. SSF 산정은 용담댐 유역을 포함한 인근 40×50 km²의 영역(N35°35'~36°00', E127°20'~127°45')에 대한 9개의 토양수분 관측지점(계북, 천천, 상전, 안천, 부귀, 주천, 장수읍, 진안읍, 무주읍)을 대상으로 연구를 수행하였으며, 이를 위해 2015년부터 2019년까지의 해당 지점의 토양수분 관측자료와 Sentinel-1A Interferometric Wide swath (IW) 모드의 Ground Range Detected (GRD) product를 구축하여 활용하였다. SSF 자료의 정확도 분석을 위한 토양수분 관측지점에 대한 LST 자료는 인근 7개 기상관측소 지점(전주, 금산, 임실, 남원, 장수, 함양군, 거창)의 관측자료로부터 역거리가중법을 통해 산정하였다. Receiver Operating Characteristic (ROC) 분석을 통한 겨울철(12-2월)의 SSF 산정 정확도를 평가한 결과, 지표면 온도 자료와의 평균 정확도는 0.75(0.48-0.87)로 나타났다. 그러나, 지표면 온도가 0℃ 이상일 때 SSF가 동결 상태로 나타나는 오차가 관측되었으며, 이는 여름철 후방산란계수의 평균값과 겨울철 후방산란계수의 평균값을 통해 산정하는 SSF 산정 수식의 특성 때문으로 이 값의 조정을 통해 오차를 개선할 수 있음을 보였다.