• Journal of Yeungnam Medical Science
• /
• v.3 no.1
• /
• pp.179-183
• /
• 1986

The frozen section technique is a means of intraoperative pathological diagnosis, and a procedure of great value to the surgeon. This method should be accurate, rapid and reliable. This method serves useful purposes, such as determining the presence of tumor, its type(especially whether it is benign or malignant), the adequacy of a biopsy of a suspected lesion, and the conditions of the surgical margins. But, it bears many disadvantages, the most of which is the danger of incorrect diagnosis. We studied the indications, the limitations, and the accuracy of the frozen section method and the materials studies was total of frozen section during recent 3 years. The overall accuracy of the frozen section diagnosis of 809 cases was 98.1% with 0.5% of false negative, 0% of false positive, 0.5% of incorrect histological diagnosis or grading errors, and 0.9% of deferred cases. The tissues submitted were lymph node, gastrointestinal tract, skin subcutaneous tissues in decreasing oder of frequency. The false positive case is not present, while the false negative cases were 4.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
• /
• v.21 no.1
• /
• pp.73-81
• /
• 1991

The purpose of this study was to analyze the position of mandibular condyle on the submentovertex radiograph, thereafter to evaluate the usefulness of submentovertex radiograph in diagnosis of temporomandibular joint dysfunction, and to check the best method of tomographic techniques. Submentovertex radiographs which were taken in 75 temporomandibular joint dysfunction patients and 75 normal persons were used as the sample for this study. The obtained results were as follows: The submentovertex radiograph was a improper method in diagnosis of temporomandibular joint dysfunction and discrimination of affected side. The selective tomography was a better method than any other tomographic techniques in diagnosis of temporomandibular joint dysfunction.

• Progress in Medical Physics
• /
• v.30 no.2
• /
• pp.49-58
• /
• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.10B
• /
• pp.1062-1069
• /
• 2009

The kidney diseases is a big social problem what is suffering sequela of metabolic syndrome due to obesity. Therefore, it is most important that early to take the appropriate action; it does not have symptoms Abnormalities of the kidney. With this, in mind, this paper wish to propose the method to can diagnosis by non self-consciousness, non-imprisonment, analgesia of kidney disease through the voice analysis. To configure the entire system is developed to combines the voice analysis, watching the face color and this paper is designed the method to diagnosis kidney disease based on labial. In this paper, organized each kidney disease patients and healthy people group and we would like to analyze, compare with output in experiment morphology analysis and numerical value analysis of voice information. Secondly, auscultation theory of Oriental medicine and linguistic, phonetics analyze out interrelation to extraction peculiar elements of kidney about voice deduction deduced relation of the first formants frequency. Such result of experimentation, deduced widely to be formed the first formants frequency bandwidth value of kidney patients group than normal group. Finally, diagnosing an kidney diseases in only labial sound, calculated about misdiagnosis probability.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.38 no.2
• /
• pp.205-210
• /
• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Korean Journal of Clinical Laboratory Science
• /
• v.43 no.1
• /
• pp.1-5
• /
• 2011

The infection rate of syphilis is still increasing in the world especially in developing countries and the infection is often seen in large amounts of clinical specimens. For the diagnosis of this disease, Rapid Plasma Reagin (RPR)/Venereal Disease Research Laboratory (VDRL) has still been used as one of major primary methods to diagnose syphilis even though the test readings are somewhat subjective with high false positive rates. Recently, the automatic ARCHITECT Syphilis TP, which is based on the detection of the TP-specific antibodies, has been introduced in many laboratories. Therefore, the clinical assessment of the method is needed to provide primary diagnosis of syphilis at the moment. We evaluated 3 different manual rapid kits and ARCHITECT Syphilis TP comparing with RPR/FTA-ABS and analysed their diagnostic properties. From February 2006 to April 2008, 203 positive and 250 negative specimens, obtained from Chungbuk National University Hospital were used for the evaluation. In the evaluation between manual rapid kits, their specificities were as high as 99.2 ~ 99.6% while their sensitivities were observed with little differences; 98.0% (199/203) for Kit A, 96.6% (196/203) for Kit B, and 97.4% (197/203) for Kit S. In the case of ARCHITECT Syphilis TP test, it showed 100% specificity (250/250) and 98.5% sensitivity (249/250). Kappa values comparing with RPR/FTA-ABS were 0.978 for Kit A, 0.964 for Kit B and Kit S, and 0.987 for ARCHITECT Syphilis TP. From our evaluation, we found out that manual rapid tests and ARCHITECT Syphilis TP have very good clinical accuracies and high kappa agreements with RPR/FTA-ABS. Due to its automation and quick simultaneous diagnosis with another serological markers, we suggest that the ARCHITECT Syphilis TP is one of best suitable method for the primary diagnosis of syphilis and that it might be able to replace RPR method in the laboratories.

• KNOM Review
• /
• v.24 no.1
• /
• pp.13-19
• /
• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Imaging Science in Dentistry
• /
• v.53 no.1
• /
• pp.61-67
• /
• 2023

Purpose: The aim of this study was to objectively detect simulated tooth ankylosis using a novel method involving cone-beam computed tomography (CBCT). Materials and Methods: Tooth ankylosis was simulated in single-rooted human permanent teeth, and CBCT scans were acquired at different current levels (5, 6.3, and 8 mA) and voxel sizes (0.08, 0.125, and 0.2). In axial reconstructions, a line of interest was perpendicularly placed over the periodontal ligament space of 21 ankylosed and 21 non-ankylosed regions, and the CBCT grey values of all voxels along the line of interest were plotted against their corresponding X-coordinates through a line graph to generate a profile. The image contrast was increased by 30% and 60% and the profile assessment was repeated. The internal area of the resulting parabolas was obtained from all images and compared between ankylosed and non-ankylosed regions under different contrast enhancement conditions, voxel sizes, and mA levels using multi-way analysis of variance with the Tukey post hoc test(α=0.05). Results: The internal area of the parabolas of all non-ankylosed regions was significantly higher than that of the ankylosed regions(P<0.05). Contrast enhancement led to a significantly greater internal area of the parabolas of non-ankylosed regions (P<0.05). Overall, voxel size and mA did not significantly influence the internal area of the parabolas(P>0.05). Conclusion: The proposed novel method revealed a relevant degree of applicability in the detection of simulated tooth ankylosis; increased image contrast led to greater detectability.

• Journal of Microbiology
• /
• v.42 no.3
• /
• pp.211-215
• /
• 2004

Toxoplasmosis has been well known as an important human infection to consider especially in pregnant women. Although many serologic methods are available, the diagnosis of toxoplasmosis can be extremely difficult. The presence of increased levels of Toxoplasma-specific IgG antibodies indicates an infection, but it does not differentiate between a recent and past infection. The purpose of our study was to compare the performance of the ELISA T. gondii IgG/IgM test, a widely used enzyme-linked immunosorbent assay, to the ELISA IgG avidity method. One hundred and four serum samples (from 38 males and 66 females) were tested and evaluated from symptomatic patients (chorioretinitis, lymphadenopathy), and from women in their first trimester of pregnancy who were suspected of having toxoplasmosis, The high IgG avidity and ELISA IgG antibody levels were in agreement for 51 of the specimens (49.0％). Thirty-eight discrepant (borderline) results from the IgG avidity method were positive for IgM (3 specimens) and IgG (37 specimens). Interestingly, out of the eight serum samples that were positive for both IgG and IgM antibodies, two samples were low IgG avidity, and three samples were borderline. There was no statistically significant relation observed between the results of the IgG avidity method and the ELISA IgG test, and the IgG avidity method and ELISA IgM test (X$^2$=1.987; p=0.370 and X$^2$=2.152; p=0.341, respectively). The IgG avidity method was considered easy to perform and an acceptable approach for the differentiation of discrepant results (recent/chronic) and for the current detection of T. gondii antibodies. We concluded that the determination of IgG avidity is a helpful tool for the diagnosis of the ocular form of toxoplasmosis and it is a safe method for screening this disease in the first trimester of pregnancy.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.4
• /
• pp.276-280
• /
• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.