• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5455-5459
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• 2013

Background: The risk of malignancy index (RMI) for the evaluation of adnexal masses is a sensitive tool in certain populations. The best cut off value for RMI 1, 2 and 3 is 200. The cut off value of RMI-4 to differentiate benign from malignant lesions is 450. Our aim was to evaluate the efficiency of four different malignancy indexes (RMI1-4) in a homogeneous population. Materials and Methods: We evaluated a total of 153 non-pregnant women with adnexal masses who did not have a history of malignancy and who were above 18 years of age. Results: A cut-off value of 250 for RMI-1 provided 95.9% inter-observer agreement, yielding 95.9% specificity, 93.5% negative predictive value, 75.0% sensitivity and 82.8% positive predictive value. A cut-off value of 250 for RMI-1 showed high performance in preoperative diagnosis of invasive malignant lesions than cut-off value of 200 in our population. A cut-off value of 350 for RMI-2 provided 94.5% inter-observed agreement, yielding 94.2% specificity, 93.4% negative predictive value, 75.0% sensitivity and 77.4% positive predictive value. RMI-2 showed the higher performance when the cut-off value was set at 350 in our population. A cut-off value of 250 provided 95.2% inter-observer agreement, yielding 95.0% specificity, 93.2% negative predictive value, 75.0% sensitivity, and 88.0% positive predictive value. RMI-3 showed the highest performance to diagnose malignant adnexal masses when the cut-off value was set at 250. In our study, RMI-4 showed similar statistical performance when the cut-off value was set at 400 [(Kappa: 0.684/p=0.000), yielding 93.8% inter-observer agreement, 93.4% specificity, 93.4% negative predictive value, 75.0% sensitivity, and 75.0% negative predictive value]. Conclusions: We showed successful utilization of RMIs in preoperative differentiation of benign from malignant masses. Many studies conducted in Asian and Pacific countries have reported different cut-off values as was the case in our study. We think that it is difficult to determine universally accepted cut-off values for RMIs for common use around the globe.

• Journal of Korean Academy of Nursing
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• v.49 no.1
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• pp.26-35
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• 2019

Purpose: The aim of this study was to identify the predictive validity of the Korean Triage and Acuity Scale (KTAS). Methods: This methodological study used data from National Emergency Department Information System for 2016. The KTAS disposition and emergency treatment results for emergency patients aged 15 years and older were analyzed to evaluate its predictive validity through its sensitivity, specificity, positive predictive value, and negative predictive value. Results: In case of death in the emergency department, or where the intensive care unit admission was considered an emergency, the sensitivity, specificity, positive predictive value, and negative predictive value of the KTAS were 0.916, 0.581, 0.097, and 0.993, respectively. In case of death in the emergency department, or where the intensive or non-intensive care unit admission was considered an emergency, the sensitivity, specificity, and positive predictive value, and negative predictive value were 0.700, 0.642, 0.391, and 0.867, respectively. Conclusion: The results of this study showed that the KTAS had high sensitivity but low specificity. It is necessary to constantly review and revise the KTAS level classification because it still results in a few errors of under and over-triage. Nevertheless, this study is meaningful in that it was an evaluation of the KTAS for the total cases of adult patients who sought help at regional and local emergency medical centers in 2016.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.567-574
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• 2003

The estimation problem of predictive value of a positive test(PVP), which is assessing the accuracy of a screening test is considered. Score methods discussed by Gart and Nam(1988) are proposed for constructing confidence interval for PVP. The simulation studies are conducted in evaluating the proposed methods and existing approximate ones.

• Imaging Science in Dentistry
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• v.45 no.3
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• pp.141-145
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• 2015

Purpose: This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods: Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections $5{\mu}m$ in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (${\alpha}=0.05$). Results: Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion: The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.656-661
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• 2024

Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a method of measuring bone mineral density in a peripheral-type clinical X-ray CT using a phantom, and we propose a method of classifying osteoporosis using bone mineral density and bone structure parameters together. It segments the trabecular bone region and cortical bone region for the six sections of the phantom and calculates the average HU value of the segmented regions. By using these values, it derives an expression converting HU value to bone mineral density. It segments trabecular bone of 1 cm region in the end part of distal radius and extracts the bone mineral density and structural parameters for the trabecular bone region. We extracted bone mineral density and structural parameters for the 18 subjects each of normal and osteoporotic group. We carried out classification experiments using three classification methods; SAD, SVM, ANN. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved in the order of ANN, SVM and SAD. Also, The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved when we use the bone mineral density and structural parameters together.

• The Journal of Korean Physical Therapy
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• v.19 no.3
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• pp.41-46
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• 2007

Purpose: This study was to investigate how dose the radiography findings are to magnetic resonance (MR) image findings in the L5-S1 instability patients. The subjects of this study were comprised of eleven males and fifteen females, who had Lumbago and agreed with this research. Methods: Radiography and MR images of Lumbar spine were acquired respectively from subjects in conditions of maximum flexion and extension. The horizontal and angular displacements in lumabosacral spine radiography were used to assess the instability of lumbar spine. MR images were also used to evaluate the intervertebral disc abnormalities and change of bone marrow. Results: The results are as follows. 1. In the case of flexion transitional displacement proposed by Dupuis et al, the specificity and negative predictive value were good accuracy ($0.7{\sim}0.8$), and the negative predictive value was in average. In the case of extension displacement, the negative predictive value was about average ($0.6{\sim}0.7$), but the sensitivity, specificity and positive predictive value were below the poor (<0.6). On the other side, the specificity was about average but other things were below in the case of angular displacement. 2. In the case of flexion transitional displacement proposed by Dupuis et al., compared with the intervertebral disc abnormalities, the negative prediction value was excellent, the sensitivity good, and the specificity about average. In the case of extension, the negative prediction value was about average, but the other things were poor. On the other side the specificity and negative predictive value had good accuracy and the sensitivity and positive prediction value were below average in the case of angular displacement. Conclusion: The above results show that the radiography finding is sufficiently helpful to find the lumbar spine instability as an economic point of view.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• The Korean Journal of Cytopathology
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• v.18 no.2
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• pp.119-125
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• 2007

Urine cytology is an important screening tool for urinary tract neoplasms. Liquid-based preparation methods, such as $ThinPrep^{(R)}$, have been introduced for non-gynecological samples. We aimed to assess the diagnostic accuracy of liquid-based preparations in urine cytology by comparing the results of the conventional Cytospin preparation method for the same samples. A total of 236 cases subject to urine cytology were enrolled in this study from January 2005 to December 2005. All cases were subjected to cystoscopy and if a malignancy was suspected, a biopsy was performed. Urine cytology slides were made using the $ThinPrep^{(R)}$ preparation method and the conventional Cytospin and/or direct smear method from the individual samples. The results of urine cytology were compared with the final cystoscopic or histological diagnoses. We analyzed the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of both cytology preparation methods. A total of 236 slides made using the liquid based method were satisfactory for slide quality, whereas 5 slides (2.1%) prepared by conventional methods were unsatisfactory because of air-drying, a thick smear, or a bloody or inflammatory background. The $ThinPrep^{(R)}$ method showed 53.1% sensitivity, 92.6% specificity, a 92,6% positive predictive value, a 94.1% negative predictive value and 85,6% accuracy, while the conventional method showed 51% sensitivity, 98.4% specificity, a 92.6% positive predictive value, a 98.4% negative predictive value and 88,6% accuracy. Although the diagnostic values were equivalent between the use of the two methods, the quality of the cytology slides and the time consumed during the microscopic examination for a diagnosis were superior for the $ThinPrep^{(R)}$ method than for the conventional method. In conclusion, our limited studies have shown that the use of the liquid based preparation method is beneficial to improve the quality of slides and reduce the duration for a microscopic examination, but did not show better sensitivity, accuracy and predictive values.