• Nutrition Research and Practice
• /
• v.13 no.3
• /
• pp.247-255
• /
• 2019

BACKGROUND/OBJECTIVES: Accurate, early identification of acutely malnourished children has the potential to reduce related child morbidity and mortality. The current World Health Organisation (WHO) guidelines classify non-oedematous acute malnutrition among children under five using Mid-Upper Arm Circumference (MUAC) or Weight-for-Height Z-score (WHZ). However, there is ongoing debate regarding the use of current MUAC cut-offs. This study investigates the diagnostic performance of MUAC to identify children aged 6-24 months with global (GAM) or severe acute malnutrition (SAM). SUBJECTS/METHODS: Cross-sectional, secondary data from a community sample of children aged 6-24 months in Niger were used for this study. Children with complete weight, height and MUAC data and without clinical oedema were included. Using WHO guidelines for GAM (WHZ < -2, MUAC < 12.5 cm) and SAM (WHZ < -3, MUAC < 11.5 cm), the sensitivity (Se), specificity (Sp), predictive values, Youden Index and Receiver Operating Characteristic (ROC) curves were calculated for MUAC when compared with the WHZ reference criterion. RESULTS: Of 1161 children, 23.3% were diagnosed with GAM using WHZ, and 4.4% with SAM. Using current WHO cut-offs, the Se of MUAC to identify GAM was greater than for SAM (79 vs. 57%), yet the Sp was lower (84 vs. 97%). From inspection of the ROC curve and Youden Index, Se and Sp were maximised for MUAC < 12.5 cm to identify GAM (Se 79%, Sp 84%), and MUAC < 12.0 cm to identify SAM (Se 88%, Sp 81%). CONCLUSIONS: The current MUAC cut-off to identify GAM should continue to be used, but when screening for SAM, a higher cut-off could improve case identification. Community screening for SAM could use MUAC < 12.0 cm followed by appropriate treatment based on either MUAC < 11.5 cm or WHZ < -3, as in current practice. While the practicalities of implementation must be considered, the higher SAM MUAC cut-off would maximise early case-finding of high-risk acutely malnourished children.

• Journal of The Korean Society of Clinical Toxicology
• /
• v.18 no.2
• /
• pp.130-135
• /
• 2020

Purpose: Taking large amounts of glyphosate is life-threatening, but the amounts of glyphosate taken by patients for suicide are not known precisely. The purpose of this study was to find the predictors of large amounts of glyphosate ingestion. Methods: This retrospective study analyzed patients presenting to an emergency department with glyphosate intoxication between 2010 and 2019, in a single tertiary hospital. The variables associated with the intake amounts were investigated. The parameters were analyzed by multivariate variate logistic regression analyses and the receiver operating characteristic (ROC) curve. Results: Of the 28 patients with glyphosate intoxication, 15 (53.6%) were in the large amounts group. Univariate analysis showed that metabolic acidosis, lactic acid, and corrected QT (QTc) interval were significant factors. In contrast, multivariate analysis presented the QTc interval as the only independent factor with intoxication from large amounts of glyphosate. (odds ratio, 95% confidence interval: 1.073, 1.011-1.139; p=0.020) The area under the ROC curve of the QTc interval was 0.838. Conclusion: The QTc interval is associated significantly with patients who visit the emergency department after being intoxicated by large amounts of glyphosate. These conclusions will help in the initial triage of patients with glyphosate intoxication.

• Imaging Science in Dentistry
• /
• v.51 no.4
• /
• pp.363-372
• /
• 2021

Purpose: This systematic review and meta-analysis assessed the diagnostic accuracy of imaging examinations for the detection of peri-implant bone defects and compared the diagnostic accuracy between titanium (Ti) and zirconium dioxide (ZrO₂) implants. Materials and Methods: Six online databases were searched, and studies were selected based on eligibility criteria. The studies included in the systematic review underwent bias and applicability assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and a random-effect meta-analysis. Summary receiver operating characteristic (sROC) curves were constructed to compare the effect of methodological differences in relation to the variables of each group. Results: The search strategy yielded 719 articles. Titles and abstracts were read and 61 studies were selected for full-text reading. Among them, 24 studies were included in this systematic review. Most included studies had a low risk of bias (QUADAS-2). Cone-beam computed tomography (CBCT) presented sufficient data for quantitative analysis in ZrO₂ and Ti implants. The meta-analysis revealed high levels of inconsistency in the latter group. Regarding sROC curves, the area under the curve (AUC) was larger for the overall Ti group (AUC=0.79) than for the overall ZrO₂ group (AUC=0.69), but without a statistically significant difference between them. In Ti implants, the AUCs for dehiscence defects(0.73) and fenestration defects(0.87) showed a statistically significant difference. Conclusion: The diagnostic accuracy of CBCT imaging in the assessment of peri-implant bone defects was similar between Ti and ZrO₂ implants, and fenestration was more accurately diagnosed than dehiscence in Ti implants.

• Journal of Periodontal and Implant Science
• /
• v.52 no.3
• /
• pp.220-229
• /
• 2022

Purpose: The aim of this study was to evaluate and compare the accuracy performance of dental professionals in the classification of different types of dental implant systems (DISs) using panoramic radiographic images with and without the assistance of a deep learning (DL) algorithm. Methods: Using a self-reported questionnaire, the classification accuracy of dental professionals (including 5 board-certified periodontists, 8 periodontology residents, and 31 dentists not specialized in implantology working at 3 dental hospitals) with and without the assistance of an automated DL algorithm were determined and compared. The accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic (ROC) curves, and area under the ROC curves were calculated to evaluate the classification performance of the DL algorithm and dental professionals. Results: Using the DL algorithm led to a statistically significant improvement in the average classification accuracy of DISs (mean accuracy: 78.88%) compared to that without the assistance of the DL algorithm (mean accuracy: 63.13%, P<0.05). In particular, when assisted by the DL algorithm, board-certified periodontists (mean accuracy: 88.56%) showed higher average accuracy than did the DL algorithm, and dentists not specialized in implantology (mean accuracy: 77.83%) showed the largest improvement, reaching an average accuracy similar to that of the algorithm (mean accuracy: 80.56%). Conclusions: The automated DL algorithm classified DISs with accuracy and performance comparable to those of board-certified periodontists, and it may be useful for dental professionals for the classification of various types of DISs encountered in clinical practice.

• Journal of Korean Clinical Nursing Research
• /
• v.28 no.3
• /
• pp.270-276
• /
• 2022

Purpose: The purpose of this study was to validate the Edmonson psychiatric fall risk assessment tool (EPFRAT) for psychiatric inpatients. Methods: Data from retrospective study were collected from 670 adult inpatients in two departments of mental health medicine of a tertiary general hospital by reviewing their electronic medical records. There were 41 patients who experienced falls and 629 patients who did not experience falls during the period from January to December 2019. Data were analyzed by sensitivity, specificity, positive predictive value, negative predictive value, and a receiver-operating characteristic curve (ROC) for validity assessment using the IBM SPSS/WIN 26.0 program. Results: Factors affecting falls were the participant's age, guardian's residence, high-risk determination at the time of admission, and comorbidity. At the 85 points where the point of sum of the sensitivity and specificity was largest, the sensitivity, specificity, positive predictive value, and negative predictive value of EPFRAT were 92.7%, 79.7%, 22.9%, and 99.4%, respectively. The area under the ROC to assess the overall validity of the tool was .92 (95% CI 0.89~0.94). Conclusion: The EPFRAT was proved to be valid and reasonable for predicting falls in psychiatric inpatients. Based on the results of this study, it could be used for the assessment of high-risk patients for falls in psychiatric units.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.25 no.4
• /
• pp.312-320
• /
• 2022

Purpose: Screening serologic tests are important tools for the diagnosis of celiac disease (CD). Immunoglobulin (Ig)G anti-deamidated gliadin peptide (anti-DGP) is a relatively new autoantibody thought to have good diagnostic accuracy, comparable to that of anti-tissue transglutaminase (anti-tTG) antibody. Methods: Pediatric patients (n=86) with a clinical suspicion of CD were included. Duodenal biopsy, anti-tTG, and IgG anti-DGP antibody tests were performed. The patients were divided into CD and control groups based on the pathological evaluation of duodenal biopsies. The diagnostic accuracy of serological tests was determined. Results: IgA anti-tTG and IgG anti-DGP antibodies were positive in 86.3% and 95.4% of patients, respectively. The sensitivity, specificity, and diagnostic accuracy of the IgA anti-tTG test were 86.3%, 50.0%, and 68.6%, respectively, and those of the IgG anti-DGP test were 95.4%, 85.7%, and 90.7%, respectively. The area under the receiver operating characteristic (ROC) curve was 0.84 (95% confidence interval [CI], 0.74-0.91) for IgA anti-tTG test and 0.93 (95% CI, 0.86-0.97) for IgG anti-DGP test. The comparison of IgA anti-tTG and IgG anti-DGP ROC curves showed a higher sensitivity and specificity of the IgG anti-DGP test. Conclusion: IgG anti-DGP is a reliable serological test for CD diagnosis in children. High tTG and DGP titers in the serum are suggestive of severe duodenal atrophy. The combined use of IgA anti-tTG and IgG anti-DGP tests for the initial screening of CD can improve diagnostic sensitivity.

• The Journal of Korean Medicine
• /
• v.43 no.1
• /
• pp.1-5
• /
• 2022

Objectives: Using algometer, measure the pressure pain threshold (PPT) of the epigastric pain(心下痞硬) and calculate the cut-off value, and this can serve as the basis for prognostic diagnosis of functional dyspepsia so we would like to evaluate its diagnostic value. Methods: We investigated 353 patients with functional dyspepsia symptoms who admitted Gangnam Weedahm Oriental Hospital from February 1, 2021 to February 27, 2021. At the time of the patient's visit, an oriental medical doctor measured the pressure at the first pain point on the Algometer of (CV14), twice each, at 1minute intervals. The ROC (receiver operating characteristic) curve and the optimal cut-off value derived through the diagnosis of the (CV14) PPT value for epigastric pain(心下痞硬) and the gold standard of oriental medical doctor, it was evaluated through. Results: In 353 patients, the area under the ROC curve (AUC) was 0.909 (p=0). In addition, the optimal cutting value was 10.05 (kg/cm2), which was statistically significant. Additionally, the sensitivity of the Algometer's PPT measurement was 0.704 and the specificity was 0.884. As a result, if the PPT value of the Algometer exceeds 10.05 (kg/cm²) in terms of the optimal cutting value, it can be seen that epigastric pain(心下痞硬) is lost. Conclusion: Algometer's PPT value measurement can be a reliable test method for quantification of epigastric pain(心下痞硬) diagnosis and can be useful as an objective indicator.

• Computers and Concrete
• /
• v.32 no.3
• /
• pp.327-337
• /
• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.4
• /
• pp.687-697
• /
• 2023

Botnets, whose attack patterns are becoming more sophisticated and diverse, are recognized as one of the most serious cybersecurity threats today. This paper revisits the experimental results of botnet detection using autoencoder, a semi-supervised deep learning model, for UGR and CTU-13 data sets. To prepare the input vectors of autoencoder, we create data points by grouping the NetFlow records into sliding windows based on source IP address and aggregating them to form features. In particular, we discover a simple power-law; that is the number of data points that have some flow-degree is proportional to the number of NetFlow records aggregated in them. Moreover, we show that our power-law fits the real data very well resulting in correlation coefficients of 97% or higher. We also show that this power-law has an impact on the learning of autoencoder and, as a result, influences the performance of botnet detection. Furthermore, we evaluate the performance of autoencoder using the area under the Receiver Operating Characteristic (ROC) curve.

• Journal of The Geomorphological Association of Korea
• /
• v.27 no.3
• /
• pp.87-103
• /
• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.