• Journal of Korean Neurosurgical Society
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• v.56 no.4
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• pp.310-314
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• 2014

Objective : To analyze the clinical courses and outcomes after anterior lumbar interbody fusion (ALIF) for the treatment of postoperative spondylodiscitis. Methods : A total of 13 consecutive patients with postoperative spondylodiscitis treated with ALIF at our institute from January, 1994 to August, 2013 were included (92.3% male, mean age 54.5 years old). The outcome data including inflammatory markers (leukocyte count, C-reactive protein, erythrocyte sedimentation rate), the Oswestry Disability Index (ODI), the modified Visual Analogue Scale (VAS), and bony fusion rate using spine X-ray were obtained before and 6 months after ALIF. Results : All of the cases were effectively treated with combination of systemic antibiotics and ALIF with normalization of the inflammatory markers. The mean VAS for back and leg pain before ALIF was $6.8{\pm}1.1$, which improved to $3.2{\pm}2.2$ at 6 months after ALIF. The mean ODI score before ALIF was $70.0{\pm}14.8$, which improved to $34.2{\pm}27.0$ at 6 months after ALIF. Successful bony fusion rate was 84.6% (11/13) and the remaining two patients were also asymptomatic. Conclusion : Our results suggest that ALIF is an effective treatment option for postoperative spondylodiscitis.

• Journal of The Korean Society of Clinical Toxicology
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• v.8 no.2
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• pp.79-87
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• 2010

Purpose: The previous studies on $H_1$ antihistamine overdose have generally been limited to cases of acute doxylamine succinate (DS) poisoning, yet there have been some studies on diphenhydramine (DPH) overdosing. But many clinicians consider the two drugs to be very similar and to have similar ingredients. The purpose of this study was to clarify the toxicologic characteristics and clinical outcomes between DS and DPH poisoning/overdose. Methods: We reviewed the medical and intensive care records of the patients with acute DS or DPH poisoning and who admitted to our emergency department from January 2008 and April 2010. We collected patient information regarding the features of the poisoning and the clinical and demographic characteristics. The patients were assessed for the clinical outcomes, the GCS, the PSS (Poisoning Severity Score) and the SOFA (Sequential Organ Failure Assessment). Results: Fifty seven patients (45 cases of DS poisoning and 12 cases of DPH poisoning) were enrolled. Compared with the DS group, the DPH group had higher incidences of intubation, serious mental change, QTc prolongation and ECG conduction abnormality (p=0.041, <0.001, 0.014 and 0.044, respectively). The DPH group had a higher PSS and a longer ICU stay. The peak CPK time and the CPK normalization time were longer for the patients with rhabdomyolysis due to DS poisoning. Conclusion: Two common $H_1$ antihistamines, doxylamine and diphenhydramine, are in the same ethanolamine-structural class, but the toxico-clinical outcomes are different according to many aspects. Therefore, clinicians could take a careful approach for the differential diagnosis and management between DS and DPH poisoning.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• Journal of Acupuncture Research
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• v.22 no.1
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• pp.211-221
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• 2005

Objective : The aim of this study is to review clinical trials on the effectiveness of acupuncture in stroke rehabilitation. Methods : Computerized literature searches was carried out on three electronic databases, and hand-searching on some chinese medical journals in library of Kyung Hee Medical Center. Results : 1) Sixteen articles of clinical trials were collected and reviewed. Among these articles, randomized controlled trials were achieved in nine articles. 2) In three articles, statically significant results in improvement of mobile abilities, activities of daily life and Quality of life were reported after acupuncture treatment applied as a part of stroke rehabilitation. In three articles no statically significant changes were reported. 3) Among two articles about spasticity, One about the upper limbs and the results showed statically significant improvement of the spasticity after acupuncture treatment as stroke rehabilitation, and the other was about the lower limbs and the results showed no statically significant changes. 4) One article about acupuncture and postural control showed statically significant results suggesting that acupuncture promotes normalization of postural control after stroke.

• Journal of the Korean Orthopaedic Association
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• v.54 no.3
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• pp.269-275
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• 2019

Purpose: This study analyzed the treatment effects of drain insertion in the posteromedial portal after an arthroscopic synovectomy and posterior septum resection for pyogenic arthritis. Materials and Methods: From June 2005 to December 2016, 56 cases (55 patients, 1 case of bilateral knee) were diagnosed with pyogenic arthritis and arthroscopic treatment was performed. Fourteen patients (25.0%) were identified with causative organisms. The average follow-up period was 12.3 months, and the mean age was 67.8 years old. Twenty-four cases were males and 31 cases were females. Kellgrene-Lawrence grade (K-L grade) I was 6 patients, II was 20 patients, III was 11 patients, and IV was 18 patients. Surgery was performed through six portal. The posterior septum was removed and a drainage tube was inserted from the posterior medial side to the posterior side of the joint. The Lysholm score was used after surgery for a functional evaluation. The K-L grade at the time of admission and at the last follow-up were compared for a radiological evaluation. Results: The mean normalization period of the C-reactive protein was 59.8 days (6-164 days). Intravenous antibiotics were used for an average of 37.1 days. The results of the functional evaluation revealed a mean Lysholm score of 64.5 (30-98) in a total of 56 cases. Two reoperations was performed, but the treatment was completed with same method in all patients. The K-L grade was similar at the time of surgery and at the last follow-up (p>0.05). Conclusion: The method of drain insertion in the posteromedial portal after arthroscopic synovectomy and posterior septum resection for pyogenic arthritis is an effective technique that can obtain satisfactory results by inducing proper exudate discharge.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.4
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• pp.283-289
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• 2021

Purpose : The purpose of this study is to investigate the effect of mid frequency electrical stimulation during squat exercise on the muscle activity and muscle activity ratio of vastus medialis oblique and vastus lateralis, and to prepare scientific basic data for exercise intervention using mid frequency electrical stimulation. Methods : This study was conducted with students from University C located in Busan, and among a total of 123 subjects, 12 subjects who complained of knee joint dysfunction between 80 and 90 points using the Kujala patellofemoral score (KPS) were used. All subjects participated in the experiment for 3 days, and MVIC values were measured for normalization of muscle activity values on the first day. For the two days, participants participated in the experiment and performed squat exercise or squat exercise receiving mid-frequency electrical stimulation in random order. Measurements were taken in the squat position immediately after the squat exercise, and muscle activities of vastus medialis oblique and vastus lateralis were measured. The measured data were compared through the dependent t test, and the statistical significance level was set to .05. Results : According to the results of this study, in the case of applying mid-frequency electrical stimulation together in the ratio of vastus medialis oblique and vastus lateralis muscle activity during squat exercise, higher values were observed compared to the case of not applying mid-frequency electrical stimulation together, and statistically significant. Also, when mid-frequency electrical stimulation was applied to both vastus medialis oblique and vastus lateralis activities during the squat exercise, higher values were shown compared to the case where the mid-frequency electrical stimulation was not applied together, but there was no statistically significant difference. Conclusion : The results of this study reported that mid-frequency electrical stimulation provided to vastus medialis oblique increased the muscle activity ratio of vastus medialis oblique and vastus lateralis. Therefore, the improvement of coordination due to the enhancement of the activity of vastus medialis oblique through mid-frequency electrical stimulation will be more helpful in the treatment of patellofemoral pain syndrome patient. In addition, it is hoped that the electrical stimulation method applied to exercise will be widely used.