• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.428-430
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• 2001

This paper describes development of a preventive diagnostic system for the substation equipments. Data acquisition system, communication control unit and computer systems have been developed in the recent 2 years. The developed system is operating in 345kV U-Ryung substation for performance and environment tests.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.43-45
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• 2015

Recent progress on CT such as multi-detector row CT, oral contrast agents and multiplanar reconstruction have markedly improved the image quality as well as diagnostic performance of gastric cancer. Multiplanar reformatted images at predetermined orientations can be easily performed and embedded into routine CT protocol without increasing medical expense or labor. Currently, many institutions have adopted routine multiplanar reformatted protocols and therefore knowledge on them can improve the diagnostic accuracy of gastric cancer.

• Clinical Endoscopy
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• v.55 no.3
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• pp.319-331
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• 2022

Esophagogastroduodenoscopy (EGD) has been used to diagnose a wide variety of upper gastrointestinal diseases. In particular, EGD is used to screen high-risk subjects of gastric cancer. Quality control of EGD is important because the diagnostic rate is examiner-dependent. However, there is still no representative quality indicator that can be uniformly applied in EGD. There has been growing awareness of the importance of quality control in improving EGD performance. Therefore, we aimed to review the available and emerging quality indicators for diagnostic EGD.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.57-69
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• 2011

Researchers are continuously trying to find innovative diagnostic tests and published articles are accumulating at an enormous rate in many medical fields. Meta-analysis enables previously published study results to be reviewed and summarized; therefore, an objective assessment of diagnostic tests can be done with a meta-analysis of sensitivities and specificities. Data obtained by applying two diagnostic tests to a well-defined group of diseased patients produce a pair of sensitivity and by applying the same medical tests to a group of non-diseased subjects produce a pair of specificity. The statistical tests in the meta-analysis need to consider the correlatedness of the results from two diagnostic tests applied to the same diseased and non-diseased subjects. The associations between two diagnostic test results are often found to be unequal for the diseased and non-diseased subjects. In this paper, multivariate meta-analytic methods are studied by taking into account the different associations between correlated variables. On the basis of Monte Carlo simulations, we evaluate the performance of the multivariate meta-analysis methods proposed in this paper.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.109-117
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• 2013

This study designed a "Diagnostic Model for Management Innovation of Medium Enterprises" based on the theoretical background of success factor and management diagnosis model for management innovation of medium enterprises and suggested a measure for utilization of strategic subject and diagnostic model that enterprises can apply. Utilization of medium enterprises management innovation diagnostic model designed through this study would be of help for making a diagnosis of the capability maturity level of enterprises' current management system and improving it by establishing a challenging capability objective and building a circulation system capable of innovating enterprises. It is expected for enterprises to overcome growing pains and establish a management system capable of achieving outcome (productivity) by repeating measurement and innovation through management diagnosis. In addition, this study provides a method to produce a strategic subject, select priority of implementation and prepare an implementation road map by classifying and filtering management issues produced as a result of management diagnosis in a systematic way. If variables necessary for production of an objective weighted value of scoring and discover of elements for category of diagnostic model and elementary items as well as design of a self-diagnosis questionnaire, measurement of management outcome suggested in this study can be able to be verified and supplemented through case study in the future, it is expected to make the degree of completion as a diagnostic model elevated that may help for growth and development through innovation of medium enterprises.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.201-208
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• 2007

Purpose: We investigated whether the diagnostic performance of SPECT scintimammography (SMM) can be improved by adding computer-aided diagnosis (CAD) of ultrasonography (US). Materials and methods: We reviewed breast SPECT SMM images and corresponding US images from 40 patients with breast masses (21 malignant and 19 benign tumors). The quantitative data of SPECT SMM were obtained as the uptake ratio of lesion to contralateral normal breast. The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program. The diagnostic performance of SPECT SMM and CAD of US alone was determined using receiver operating characteristic (ROC) curve analysis. The best discriminating parameter (D-value) combining SPECT SMM and the CAD of US was created. The sensitivity, specificity and accuracy of combined two diagnostic modalities were compared to those of a single one. Results: Both SPECT SMM and CAD of US showed a relatively good diagnostic performance (area under curve = 0.846 and 0.831, respectively). Combining the results of SPECT SMM and CAD of US resulted in improved diagnostic performance (area under curve =0.860), but there was no statistical differerence in sensitivity, specificity and accuracy between the combined method and a single modality. Conclusion: It seems that combining the results of SPECT SMM and CAD of breast US do not significantly improve the diagnostic performance for diagnosis of breast cancer, compared with that of SPECT SMM alone. However, SPECT SMM and CAD of US may complement each other in differential diagnosis of breast cancer.

• Journal of Genetic Medicine
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• v.8 no.1
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• pp.28-34
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• 2011

Most clinicians understand clinical trials as the evaluation process for new medicine before their use. However, clinical trials can also be applied to laboratory diagnostic tests (LDTs) to verify diagnostic accuracy and efficacy before their clinical laboratory implementation for patients. The clinical trial of LDT has two distinctive characteristics that are different from the case of pharmaceuticals and thus worth special consideration. One of them is the level of evidence. The well-designed randomized controlled trials (RCTs) are known to provide the best evidence to prove the clinical efficacy of any pharmaceutical products. However, RCTs lose practicality when applied to LDTs due to various issues including ethical complications. For this reason, comparative study format is considered more feasible approach for LDTs. In addition pharmaceuticals and LDTs are different in that the user's intervention is not required for the former but critical to the latter. Moreover, in the case of pharmaceuticals, end-products are produced by manufacturers before being used by clinicians. However, in LDTs, once reagents and instruments are provided by manufacturers, they are first utilized by clinical laboratories to produce test results in order for clinicians to use them later. In other words, when it comes to LDTs, clinical laboratories play the role of manufacturers, providing reliable test results with improved quality assurance. Considering the distinctive characteristics of LDTs, we would like to offer detailed suggestions to successfully perform clinical trials in LDTs, which include analytical performance measures, clinical test performance measures, diagnostic test accuracy measures, clinical effectiveness measures, and post-implementation surveillance.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1240-1252
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• 2021

Objective: To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis. Materials and Methods: Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies. Results: Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86-0.93) and 0.90 (95% CI 0.84-0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68-0.83) and 0.83 (95% CI 0.73-0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93-0.97) for DBT and 0.86 (95% CI 0.82-0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses. Conclusion: Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.149-159
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• 2011

In this paper, we study the problem of how to design a medical-grade wireless local area network (WLAN) for healthcare facilities. First, unlike the IEEE 802.11e MAC, which categorizes traffic primarily by their delay constraints, we prioritize medical applications according to their medical urgency. Second, we propose a mechanism that can guarantee absolute priority to each traffic category, which is critical for medical-grade quality of service (QoS), while the conventional 802.11e MAC only provides relative priority to each traffic category. Based on absolute priority, we focus on the performance of real-time patient monitoring applications, and derive the optimal contention window size that can significantly improve the throughput performance. Finally, for proper performance evaluation from a medical viewpoint, we introduce the weighted diagnostic distortion (WDD) as a medical QoS metric to effectively measure the medical diagnosability by extracting the main diagnostic features of medical signal. Our simulation result shows that the proposed mechanism, together with medical categorization using absolute priority, can significantly improve the medical-grade QoS performance over the conventional IEEE 802.11e MAC.