• 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.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.1
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• pp.243-272
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• 2024

This study was conducted with the purpose of providing suggestions for improvement through a comparison and analysis of the services of the U.S. National Library of Medicine, the world's largest medical library, and the National Center for Medical Information and Knowledge, Korea's national medical library. Core services that need to be improved are topic-specific services, community services, services by user type, educational services, technology, facility/space services, research support services, and marketing and public relations and cooperation services. Specialized libraries are also increasingly interested in topic-specific services and public services. Efficiency in access through services for each type of user is needed, and various types of educational services that do not limit the target audience are also needed. Marketing through AI, virtual reality, and technology, facility, and space services to support the disabled, research support services centered on research ethics, research grants, and programs, and collaborative services with domestic and international libraries, academic societies, institutions, and local communities in other related fields and publicity are also needed.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1743-1748
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• 2021

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1241-1250
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• 2022

Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and Methods: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Results: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). Conclusion: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.