• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.281-285
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• 2013

Purpose: To probe the role of FasL in cell apoptosis in oral squamous cell carcinomas (OSCCs). Methods: The expression of Fas/FasL was assessed in 10 cases of normal oral epithelium, 38 cases of OSCC and tumor infiltrating lymphocytes (TIL), and 11 cases of metastatic lymph nodes by immunohistochemistry. Apoptosis of tumor cells and TIL was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay (TUNEL). FasL-induction of T cell apoptosis was tested by co-culture assay in vitro with SCC-9 and Jurkat T cells. Results: The 10 cases of normal oral epithelium all demonstrated extensive expression of Fas, the positive rate being largely down-regulated in OSCC (21/38) (P<0.05) compared to the normal (10/10). At the same time, the positive rate of FasL significantly increased in OSCC (P<0.05) especially those with lymph node metastasis (P<0.05). The positive rates of Fas in well and middle differentiated OSCC were higher than those in poor differentiated OSCC (P<0.05). The AI of tumor cells in Fas-positive OSCC was remarkably higher than that in Fas-negative OSCC (P<0.01), with a positive correlation between Fas expression and cell differentiation as well as apoptosis (r=0.68, P<0.01). The AI of tumor cells in FasL positive OSCC was remarkably lower than that in control while the AI of TIL was higher than in FasL negative OSCC (P<0.05). The AI of tumor cells reversely correlated with that of TIL (r = -0. 72, P<0.05). It was found that SCC-9 cells expressing functional FasL could induce apoptosis of Jurkat cells as demonstrated by co-culture assays. As a conclusion, it is evident that OSCC cells expressing FasL can induce apoptosis in Fas-expressing T cells. Conclusions: In progression of OSCC, expression of the Fas/FasL changes significantly. The results suggest that FasL is a mediator of immune privilege in OSCC and may serve as an marker for predicting malignant change in oral tissues.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.2
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• pp.1-13
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• 2023

Purpose : Along with the rapid development of digital technology, the application of digital healthcare in the medical field is also increasing. According to many experts, increasing the amount of exercise and physical activity is a helpful way to prevent and manage physical problems in modern society. However, a lack of exercise, which is of the lifestyle of modern people, leads to the development of various diseases. This study aimed to examine the effects of digital exercise intervention using artificial intelligence (AI) on the physical abilities of adults whether digital exercise intervention can be a reliable and effective therapeutic option for musculoskeletal disorders in real-world clinical settings. Methods : In this study, exercise was conducted using a digital application to investigate the effects of an AI-based digital exercise intervention on the physical abilities of adults. A total of 13 adults were evaluated, and their physical abilities before and after the exercise intervention were compared. Hand-grip strength, functional leg muscle strength, dynamic balance, and quadriceps muscle strength were assessed. Exercise was performed using a digital application and in a non-face-to-face manner. AI identified the exercise status of each participant and adjusted the exercise difficulty level accordingly. The exercised daily for 4 weeks. Results : A total of 12 participants were analyzed for the final results. Significant improvements were observed in hand-grip strength, functional leg muscle strength (evaluated using the stand-up test), dynamic balance, and straight-gait ability (p<.05), indicating an increase in the overall muscular strength and physical function of the participants. Conclusions : Digital exercise intervention using AI is effective in improving physical abilities related to musculoskeletal function. It can be useful in clinical practice as an effective treatment option for patients with musculoskeletal disorders or muscle weakness.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• BMB Reports
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• v.47 no.4
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• pp.203-208
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• 2014

To gain insights into the effect of MexB gene under the short interfering RNA (siRNA), we synthesized 21 bp siRNA duplexes against the MexB gene. RT-PCR was performed to determine whether the siRNA inhibited the expression of MexB mRNA. Changes in antibiotic susceptibility in response to siRNA were measured by the E-test method. The efficacy of siRNAs was determined in a murine model of chronic P. aeruginosa lung infection. MexB-siRNAs inhibited both mRNA expression and the activity of P. aeruginosa in vitro. In vivo, siRNA was effective in reducing the bacterial load in the model of chronic lung infection and the P. aeruginosa-induced pathological changes. MexB-siRNA treatment enhanced the production of inflammatory cytokines in the early infection stage (P < 0.05). Our results suggest that targeting of MexB with siRNA appears to be a novel strategy for treating P. aeruginosa infections.

• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.427-428
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• 1999

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.113-126
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• 2019

Based on the accumulation of medical big data, advances in medical artificial intelligence technology facilitate the timely treatment of disease through the reading the medical images and the increase of prediction speed and accuracy of diagnoses. In addition, these advances are expected to spark significant innovations in reducing medical costs and improving care quality. There are already approximately 40 FDA approved products in the US, and more than 10 products with K-FDA approval in Korea. Medical applications and services based on artificial intelligence are expected to spread rapidly in the future. Furthermore, the evolution of medical artificial intelligence technology is expanding the boundaries or limits of various related issues such as reference standards and specifications, ethical and clinical validation issues, and the harmonization of international regulatory systems.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.