• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.313-323
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• 2022

The purpose of this study was to explore ways a digital therapeutics-based community care plans for the rehabilitation in dysarthria in a situation where non-face-to-face services are expanding in the COVID-19 era. To this end, a qualitative study was conducted on experts working in hospitals, speech-language pathology centers, and social welfare centers, and as a result of the study, 3 topics, 9 sub-themes, and 18 content units were derived. Based on the analysis results, the digital therapeutics-based community care model consisted of 9 types: remote diagnosis, telepractice, rehabilitation training program, peer supporters, clinical support, communication, psychosocial intervention, and care plan services. This study will be able to provide basic data for health care & welfare services using digital therapeutics and guidelines for establishing shared care plans based on multidisciplinary cooperation.

• Korean Journal of Clinical Pharmacy
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• v.32 no.1
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• pp.37-46
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• 2022

Background: Non-valvular atrial fibrillation (NVAF) is associated with ischemic stroke risk in the aging population. Observational studies have indicated beneficial effects of direct-acting oral anticoagulant (DOAC) against ischemic stroke compared to warfarin. This study aimed to investigate ischemic stroke incidence and bleeding risk in patients on DOAC therapy. Methods: Using the database of Korean Health Insurance Review and Assessment-Aged Patient Sample 2015, we conducted a retrospective cohort study. Study subjects with NVAF diagnosis and prescribed anticoagulants were enrolled. Propensity score (PS) matching by age, sex, comorbidities, and medications were used. The clinical outcomes were major adverse cerebro-cardiovascular events (MACCEs, ischemic stroke/systemic embolism, myocardial infarction, cardiac death) and bleeding events. A cox proportional hazard model analysis was performed to compare the outcomes with hazard ratio (HR) and 95% confidence interval (CI). Results: Total 4,773 elderly patients with NVAF were initially included. Four PS-matched groups including rivaroxaban vs. warfarin-only (n=1,079), dabigatran vs. warfarin-only (n=721), rivaroxaban vs. dabigatran (n=721), and switchers of warfarin to rivaroxaban vs. warfarin-only (n=287) were analyzed. Every group showed statistically similar results of MACCEs and bleeding events, except for the group of rivaroxaban vs. dabigatran. Rivaroxaban users showed higher risks of bleeding events than dabigatran users (HR 2.25, 95% CI 1.01-4.99). Conclusion: In the elderly patients with NVAF, efficacy and safety outcomes among oral anticoagulants including DOACs and warfarin were similar, while rivaroxaban are more likely to have higher bleeding risks than dabigatran. Further research using large size sample is needed.

• Physical Therapy Korea
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• v.30 no.2
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• pp.136-143
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• 2023

Background: The International Classification of Functioning, Disability and Health (ICF) model, created by the World Health Organization, provides a theoretical framework that can be applied in the diagnosis and treatment of various disorders. Objects: Our research purposed to ascertain the relationship between structure/function, activity, and participation domain variables of the ICF and pain, pain-associated disability, activities of daily living (ADL), and quality of life in patients with chronic low back pain (LBP). Methods: Two-hundred patients with chronic LBP (mean age: 35.5 ± 8.8 years, females, n = 40) were recruited from hospital and community settings. We evaluated the body structure/function domain variable using the Numeric Pain Rating Scale (NPRS) and Roland-Morris disability (RMD) questionnaire. To evaluate the activity domain variable, we used the Oswestry Disability Index (ODI) and Quebec Back Pain Disability Scale (QBDS). For clinical outcome measures, we used Short-form 12 (SF-12). Pearson's correlation coefficient was used to ascertain the relationships among the variables (p < 0.05). All the participants with LBP received 30 minutes of conventional physical therapy 3 days/week for 4 weeks. Results: There were significant correlations between the body structure/function domain (NPRS and RMD questionnaire), activity domain (ODI and QBDS), and participation domain variables (SF-12), rending from pre-intervention (r = -0.723 to 0.783) and postintervention (r = -0.742 to 0.757, p < 0.05). Conclusion: The identification of a significant difference between these domain variables point to important relationships between pain, disability, performance of ADL, and quality in participants with LBP.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.459-464
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• 2023

Due to the development of advanced technology, a lot of digital radiographic equipment has been developed, which is very helpful for accurate diagnosis and treatment, and it is very important to train personnel who have acquired professional knowledge in order to use it safely and effectively. Students are exposed to the risk of radiation exposure in radiography training using diagnostic X-ray equipment, and some educational institutions do not use X-ray equipment due to management difficulties in accordance with the Nuclear Safety Act. As a solution to this, this study developed a medical radiation simulator for education that does not generate radiation by using a vision sensor and self-developed software. Through this, educational institutions can reduce the burden of administrative implementation according to the law, and students can obtain a high level of educational effects in a healthy practice environment without radiation exposure.

• The Korea Journal of Herbology
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• v.38 no.6
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• pp.29-44
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• 2023

Objectives : This study was planned to evaluate the therapeutic effectiveness and possible underlying mechanism of TPE (Tetrapanax papyrifer stem(inner part of the stem Extract) and AQE (Akebiae quinata stem Extract) on osteoarthritis. Methods : Osteoarthritis models were induced through intra-articular injection of MIA (monosodium iodoacetate) 50 μL with 80 mg/ml in rats. Excluding the normal group, Osteoarthritis-induced rats were divided into 4 groups (Control, INDO, TPE, AQE). The drug concentrations were indomethacin 5 mg/kg, TPE 200 mg/kg, and AQE 200 mg/kg, and were orally administered once a day for a couple of weeks. After drug supplementation, the effects of TPE and AQE were measured with serum diagnosis, western blotting, and histopathological staining. Results : It was found that the DPPH and ABTS free radical erasure ability of AQE was better than that of TPE. AQE administration improved rear limb overload and it led to relieving pain. Both PTE and AQE significantly reduced the expression of inflammatory mediators COX-2, iNOS, and inflammatory cytokine IL-1β and IL-6 by inhibiting the phosphorylation of IκBα and deactivating the pathway of NF-κBp65. On the other hand, TNF-α was significantly reduced only by administration of AQE. In addition, histopathological analysis showed that the administration of AQE compared to PTE suppressed cartilage degeneration and effectively suppressed damage to proteoglycan, a component of ECM. Conclusion : Reviewing these experimental results, TPE and AQE possessed the effect of delaying the progress of osteoarthritis and protecting cartilage. In addition, the results of this study show that AQE has a better cartilage protection effect than TPE.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Imaging Science in Dentistry
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• v.53 no.4
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• pp.271-281
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• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1157-1170
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• 2023

Ascites is a condition in which body fluids are abnormally accumulated in the fish's abdominal cavity, and is an important indicator of the health of flounder. Ascites can occur in the process of infection with bacteria, viruses, parasites, etc., which causes abdominal distension, sluggish growth, and weight loss. In this paper, we tried to find the correlation with other symptoms or diseases that affect ascites symptoms in flounder. As experimental data, ascites symptoms were divided into three states: no ascites, ascites transparent, and ascites opaque, and disease diagnosis data of cultured flounder collected for 7 years were used. After performing an appropriate preprocessing process for the random forest machine learning method, other symptoms and disease factors related to ascites were extracted, and it was confirmed that the proposed model could present the main factors related to ascites.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.19-32
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• 2024

An investigation into the detailed safety diagnosis report indicates that domestic highway bridges mainly suffer from defects, deterioration, and damage due to physical forces. In particular, deterioration is an inevitable damage that occurs due to various environmental and external factors over time. In particular, bridge deck is very vulnerable to cracks, which occur along with various types of damages such as rebar corrosion and surface delamination. Thus, this study evaluates a correlation between heterogeneous damage and deterioration environment and then identifies the main causes of such heterogeneous damage. After all, a bridge heterogeneous damage prediction model was developed using random forests to determine the top five factors contributing to the occurrence of the heterogeneous damage. The results of the study would serve as a basic data for estimating bridge maintenance and budget.