• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.19.1-19.6
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• 2023

Rapid immunochromatography test (RICT) kits are commonly used for the diagnosis of canine parvovirus (CPV) because of their rapid turnaround time, simplicity, and ease of use. However, the potential for cross-reactivity and low sensitivity can yield false-positive or false-negative results. There are 4 genotypes of CPV. Therefore, evaluating the performance and reliability of RICT kits for CPV detection is essential to ensure accurate diagnosis for appropriate treatment. In this study, we evaluated the performance of commercial RICT kits in the diagnosis of all CPV genotypes. The cross-reactivity of 6 commercial RICT kits was evaluated using 8 dog-related viruses and 4 bacterial strains. The limit of detection (LOD) was measured for the 4 genotypes of CPV and feline panleukopenia virus. The tested kits showed no cross-reactivity with the 8 dog-related viruses or 4 bacteria. Most RICT kits showed strong positive results for CPV-2 variants (CPV-2a, CPV-2b, and CPV-2c). However, the 2 kits produced negative results for CPV-2 or CPV-2b at a titer of 10⁵ FAID₅₀/mL, which may result in inaccurate diagnoses. Therefore, some kits need to improve their LOD by increasing their binding efficiency to detect all CPV genotypes.

• Journal of Korean society of Dental Hygiene
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• v.4 no.2
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• pp.277-291
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• 2004

The purpose of this study is to analyze the dental hygienists' overall performance in operative restoration and the clinical performance in operative restoration according to dental hygienists' career and to provide basic data for establishing the appropriate range of dental hygienists' work. Subjects of this study are 339 dental hygienists working at dental clinic and hospital nationwide, selected by their working place, career, type of clinic, and location of clinical institution. The distribution of people who responded to the survey shows that 81 belong to beginner level(less than 2 years since entering clinic), 115 intermediate level(2 to 3 years since entering clinic), 81 higher level(4 to 5 years since entering clinic) and 62 advanced level(more than 6 years since their entering clinic). In terms of the types of clinical institution, 178 belong to dental clinics and 161 belong to dental hospitals. The survey used in this study are focused on perception about clinical performance in operative dentistry and adequacy of the work. Operative dentistry consists of operative restoration and endodontic therapy. The operative restoration consists of 15 categories such as patient welcoming, examination and diagnosis, planning of treatment, anesthesia, control of moisture, cavity preparation, pulp protection, matrix band application, amalgam filling, resin filling, glass ionomer cement filling, abrasive strip removal, rubber dam removal, bite check and polishing, patient education, and arrangement. The reliability was Cronbach's Alpha .9453. SPSS 10.0 for Windows was used to analyze the responses. One way ANOVA was utilized to verify the differences in the dental hygienists' job performance in operative restoration and their job performance according to career. When significant difference was found. Duncan multi comparison post hoc was done. To sum up the results of this study, patient welcoming look the first place in the operative restoration. It was followed by patient education, examination and diagnosis, introducing treatment plan, resin filling, glass ionomer cement filling, amalgam filling, bite check and polishing, anesthesia, pulp protection, control of moisture, abrasive strip removal, cavity preparation, matrix band application, rubber dam removal, and anesthesia. In terms of the clinical performance by career, there were significant differences in 19 activities such as medical eraluation, oral examination, patient charting, intra oral readio graphs, firm developing fixing mounting, curing light gun, education of attention content after operation. Based on the results of this study, the specific range of operative restoration for dental hygienists should be focused on providing basic data for dentists' diagnosis, alleviation of fear and aching accompanied by injection and anesthesia, data providing for dentists' decision of anesthesia degree, and maximization of control of moisture.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.162-168
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• 2007

For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.3
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• pp.1-15
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• 2011

Objectives: We describe the development of a smoking and drinking prevention program for adolescents, using intervention mapping. Methods: The study sample consisted of 1,000 high school second-grade students from 6 high schools in Seoul. The PRECEDE model was applied for the needs assessment. We carried out a social diagnosis by assessing the factors such as the quality of life, happiness level, and satisfaction with school life; an epidemiological diagnosis on the perceived health status, stress levels, and priority of health issues; a behavioral diagnosis on the smoking and drinking rate and the intention to smoke and drink; and an educational diagnosis on knowledge, beliefs, attitudes, self-efficacy, outcome expectations, social norms and life skills. Results: The development process included a needs assessment, identifying factors that influence smoking and drinking among adolescents. Intention, knowledge, perceived norms, perceived benefit, perceived cost, perceived susceptibility, self-efficacy, and life skills were identified as determinants. Three performance objectives were formulated to describe what an individual needs to do in order to avoid smoking and drinking. Subsequently, we constructed an intervention matrix by crossing the performance objectives with the selected determinants. Each cell describes the learning objectives of the smoking and drinking prevention program. The program used methods from the transtheoretical model, such as consciousness raising, outcome expectations, self-reevaluation, self-liberation, counterconditioning, environmental reevaluation, and stimulus control. The program deals with the effects of smoking and drinking, self-improvement, decision making, understanding advertisements, communication skills, social relationships, and assertiveness. Conclusions: By using the process of intervention mapping, the program developer was able to ensure a systematical incorporation of empirical and new data and theories to guide the intervention design. Programs targeting other health-related behavior and other methods or strategies can also be developed using this intervention mapping process.

• Journal of Preventive Medicine and Public Health
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• v.43 no.1
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• pp.42-49
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• 2010

Objectives: To compare the performance of three International Statistical Classification of Diseases, 10th Revision translations of the Charlson comorbidities when predicting in-hospital among patients with myocardial infarction (MI). Methods: MI patients ${\geq}20$ years of age with the first admission during 2006 were identified(n=20,280). Charlson comorbidities were drawn from Heath Insurance Claims Data managed by Health Insurance Review and Assessment Service in Korea. Comparisions for various conditions included (a) three algorithms (Halfon, Sundararajan, and Quan algorithms), (b) lookback periods (1-, 3- and 5-years), (c) data range (admission data, admission and ambulatory data), and (d) diagnosis range (primary diagnosis and first secondary diagnoses, all diagnoses). The performance of each procedure was measured with the c-statistic derived from multiple logistic regression adjusted for age, sex, admission type and Charlson comorbidity index. A bootstrapping procedure was done to determine the approximate 95% confidence interval. Results: Among the 20,280 patients, the mean age was 63.3 years, 67.8% were men and 7.1% died while hospitalized. The Quan and Sundararajan algorithms produced higher prevalences than the Halfon algorithm. The c-statistic of the Quan algorithm was slightly higher, but not significantly different, than that of other two algorithms under all conditions. There was no evidence that on longer lookback periods, additional data, and diagnoses improved the predictive ability. Conclusions: In health services study of MI patients using Health Insurance Claims Data, the present results suggest that the Quan Algorithm using a 1-year lookback involving primary diagnosis and the first secondary diagnosis is adequate in predicting in-hospital mortality.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1415-1425
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• 2016

This paper presents a new approach for fault diagnosis in three-level neutral point clamped inverters. The proposed method is based on the average values of the positive and negative parts of normalized output currents. This method is capable of detecting and locating multiple open-circuit faults in the controlled power switches of converters in half of a fundamental period of those currents. The implementation of this diagnostic approach only requires two output currents of the inverter. Therefore, no additional sensors are needed other than the ones already used by the control system of a drive based on this type of converter. Moreover, through the normalization of currents, the diagnosis is independent of the load level of the converter. The performance and effectiveness of the proposed diagnostic technique are validated by experimental results obtained under steady-state and transient conditions.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.1
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• pp.87-92
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• 2016

In this paper, we improved classification performance of benign and malignant lung nodules by including the parenchyma features. For small pulmonary nodules (4-10mm) nodules, there are a limited number of CT data voxels within the solid tumor, making them difficult to process through traditional CAD(computer aided diagnosis) tools. Increasing feature extraction to include the surrounding parenchyma will increase the CT voxel set for analysis in these very small pulmonary nodule cases and likely improve diagnostic performance while keeping the CAD tool flexible to scanner model and parameters. In AdaBoost learning using naive Bayes and SVM weak classifier, a number of significant features were selected from 304 features. The results from the COPDGene test yielded an accuracy, sensitivity and specificity of 100%. Therefore proposed method can be used for the computer aided diagnosis effectively.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.283-294
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• 2005

The Center for Imaging Human Structures (CIH) was established in December 2002 to develop new diagnostic imaging techniques and to make them available to the greater community of biomedical and clinical researchers at Sungkyunkwan University. CIH has been involved in 5 specific activities to provide solutions for early diagnosis and improved treatment of human diseases. The five area goals include: 1) development of a digital mammography system with computer aided diagnosis (CAD); 2) development of digital radiological imaging techniques; 3) development of unified medical solutions using 3D image fusion; 4) development of multi-purpose digital endoscopy; and, 5) evaluation of new imaging systems for clinical application

• Journal of Digestive Cancer Research
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• v.12 no.1
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• pp.31-37
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• 2024

Pancreatic cancer is a highly fatal malignancy with a 5-year survival rate of < 10%. Endoscopic ultrasound (EUS) is a useful noninvasive tool for differential diagnosis of pancreatic malignancy and treatment decision-making. However, the performance of EUS is suboptimal, and its accuracy for differentiating pancreatic malignancy has increased interest in the application of artificial intelligence (AI). Recent studies have reported that EUS-based AI models can facilitate early and more accurate diagnosis than other preexisting methods. This article provides a review of the literature on EUS-based AI studies of pancreatic malignancies.