• The Korean Journal of Pain
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• v.14 no.2
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• pp.218-224
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• 2001

Background: Response to diagnostic blocks does not consistently predict the outcome of interventional facet denervation. We investigated the relationship between pain relief by the percutaneous radiofrequency denervation of the lumbar zygapophysial joints with the result of facet joint diagnostic local anesthetic injection in patients with back pain originating from the lumbar zygapophysial joint. Methods: There were 35 patients enrolled, with ranging in age from 25 to 76 years ($52.6{\pm}12.7$ years, mean ${\pm}$ SD). We studied 7 men (20%) and 28 women (80%). All patients underwent double diagnostic block of $L_{3/4}$, $L_{4/5}$ and $L_5-S_1$ facet joint with 0.5% bupivacaine. The 35 patients fell into the following group. (1) Group A (n = 16): those who felt clear relief (pain free with Likert scale) from the double diagnostic block (2) Group B (n = 19): 11 patients who were always equivocal in their response to the double diagnostic block and 8 patients who were either pain free or equivocal in their response to the double diagnostic block. All 11 patients were done the facet joint denervation. The effect on the pain was evaluated with 4 point Likert scale 1, 6 and 12 weeks after the procedure. We evaluated the relationship between the pain response to diagnostic block and the pain relief with facet joint denervation. Results: Significant correlation was observed between the response to diagnostic block and pain relief with facet denervation (P < 0.05). We found no correlation between the categories of spinal operation and pain response to facet denervation (P value > 0.05). Conclusions: A satisfactory result of lumbar facet joint denervation can be obtained in many patients, especillay in patients whose pain were relieved by the diagnostic double facet joint block. It may be said that facet joint denervation for mechanical low back pain using radiofrequency thermocoagulation is a safe, easy, and repeatable technique.

• The Journal of Korean Obstetrics and Gynecology
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• v.35 no.2
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• pp.42-53
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• 2022

Objectives: The definition of Sanhupung (Puerperal wind disorder) has been varied and there has been a prior study to establish the definition, but no clear conclusion has been reached on diagnostic criteria. Therefore, the aim of this study was to clearly redefine the definition of Sanhupung using the Delphi method. Methods: This study used the Delphi technique. A panel consisting of 13 experts of Korean medicine, particularly in Obstetrics & Gynecology, participated in the Delphi survey that included answering the 3rd round survey. The Delphi survey was conducted by evaluating and correcting the questionnaire using e-mail. Results: Through the Delphi survey, we have reached an agreement regarding the diagnostic criteria of Sanhupung. They are as follows: 1) Sanhupung can be diagnosed based on basic symptoms. If one or more symptoms are expressed in the basic symptom group, it can be diagnosed as Sanhupung. It is diagnosed in detail as a pain type or a sensory impairment type according to the category of basic symptoms. 2) Incidental symptoms are not essential for diagnosis, and are referred to for checking general weakness and autonomic nervous system conditions. 3) In order to meet the diagnostic criteria, the symptoms should occur within 6 months after childbirth or miscarriage, and the cause of the symptoms should not be classified as other diseases. Conclusions: The diagnostic criteria of Sanhupung were suggested based on the Delphi survey among experts in the field. Further research is necessary to improve the reliability and validity of the criteria.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.211-216
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• 2017

This paper proposes image processing techniques that improve usability and performance in a diagnostic system of the contrast-enhanced ultrasonography. For a methodology for visualizing diagnostic parameter data in an ultrasonic medical image, an expression of transition time data with successive pixel values and a method of generating a lesion diagnostic parameter image with four categorized values are presented. We also introduce a MRF-based image enhancement technique to eliminate noises from generated parametric images. Such parametric image generation technique can overcome the difficulty of discriminating dynamic change in patterns in the ultrasonography. The technique clarifies the contour of the region in the original image and facilitates visual determination of the characteristics of the lesion through four colors. With regard to this MRF-based image enhancement, we define the energy function of consecutive pixel values and develop a technique to optimize it, and the usability of the proposed theory is examined through experiments with medical images.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.75-83
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• 2004

With the rotating machinery getting more accurate and diversified, the necessity fur an appropriate diagnosis technique and maintenance system has been greatly recognized. However, until now, the operator has executed a monitoring of the machine by the senses or simple the change of RMS (root mean Square) value. So, the diagnostic expert system using the fuzzy inference which the operator can judge easily and expertly a condition of the machine is developed through this study. In this paper, the hardware and software of the diagnostic expert system was composed and the identification of the diagnostic performance of the developed system for 5 fault phenomena was carried out.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.3
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• pp.54-59
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• 2007

It is widely recognized that increasing the accuracy and diversity of rotating machinery necessitates an appropriate diagnostic technique and maintenance system. Until now, operators have monitored machinery using their senses or by analyzing simple changes to root mean square output values. We developed an expert diagnostic system that uses fuzzy inference to expertly assess the condition of a machine and allow operators to make accurate judgments. This paper describes the hardware and software of the expert diagnostic system. An assessment of the diagnostic performance for five fault phenomena typically found in pumps is also described.

• Parasites, Hosts and Diseases
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• v.60 no.4
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• pp.295-299
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• 2022

Malaria elimination and control require prompt and accurate diagnosis for treatment plan. Since microscopy and rapid diagnostic test (RDT) are not sensitive particularly for diagnosing low parasitemia, highly sensitive diagnostic tools are required for accurate treatment. Molecular diagnosis of malaria is commonly carried out by nested polymerase chain reaction (PCR) targeting 18S rRNA gene, while this technique involves long turnaround time and multiple steps leading to false positive results. To overcome these drawbacks, we compared highly sensitive cytochrome oxidase gene-based single-step multiplex reaction with 18S rRNA nested PCR. Cytochrome oxidase (cox) genes of P. falciparum (cox-III) and P. vivax (cox-I) were compared with 18S rRNA gene nested PCR and microscopy. Cox gene multiplex PCR was found to be highly specific and sensitive, enhancing the detection limit of mixed infections. Cox gene multiplex PCR showed a sensitivity of 100% and a specificity of 97%. This approach can be used as an alternative diagnostic method as it offers higher diagnostic performance and is amenable to high throughput scaling up for a larger sample size at low cost.

• Journal of Veterinary Clinics
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• v.39 no.6
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• pp.326-333
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• 2022

The purpose of this study was to evaluate the radiographic positioning techniques used in local animal hospitals, identify the most common positioning problem, and determine whether there were changes after teleradiology consultations. From September 2016 to April 2021, 15 local animal hospitals that requested radiographic interpretation more than 10 times and for more than six cases of thoracic radiographs were selected. Six sets of two-views of (lateral and ventrodorsal [VD] or dorsoventral [DV] views) digital thoracic radiographs from six dogs per hospital were evaluated in four categories. For the lateral view, radiographic technique scores used in ten local animal hospitals improved, one remained the same, and four became worse. For the VD/DV view, the score for eleven hospitals improved and worsened for four. The most common problem was rotation (57/90) for the lateral view, followed by an inappropriate field of view (59/90), and incorrect beam center (71/90). For the VD/DV view, an inappropriate field of view (54/90) was the most common problem, followed by asymmetry (63/90), and incorrect beam center (73/90). Every factor, except rotation in the lateral view, improved after obtaining technical consultation; however, the degrees of improvement were not remarkable. There was no significant correlation between the number of requests and the degree of improvement. According to the results, the radiographic technique used in local animal hospitals was improved by technical advice on teleradiology. These changes make it possible to provide accurate diagnoses of the requested images. There are some limitations regarding the indicators of evaluation and the number of cases; therefore, further studies that use detailed indicators in large cohort group are needed. In addition, an effective method of teaching should be developed to improve radiographic techniques in local animal hospitals.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.111-117
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• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• Smart Structures and Systems
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• v.22 no.5
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.965-970
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• 2011

We develop a diagnostic system to monitor failures in an engine-assembly line. Existing techniques such as sensory analysis, time domain analysis, frequency analysis, and statistical analysis have limitations in the diagnosis of engine-assembly failure when there are abnormal vibration waveforms (crashing and damping signals) during the assembly. We use a wavelet technique to deal with crashing and damping signals. We also implement a new technique for developing diagnostic rules from sensor data, and we demonstrate its validity.