• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1145-1152
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• 2015

This study examined the serum lipid levels according to the obesity indices, and obtained the relationship between obesity indices and serum lipid levels among adults who underwent a health checkup in a university hospital. The subjects for this study were 4,112 adults aged 18 to 77 years who underwent medical examinations at the health center of a university hospital in Daejeon city from Jan 2012 to Dec 2013. The serum lipid levels (TC, HDL-C, LDL-C, TG, AI) and obesity indices (height, weight, waist circumference, body fat rate, BMI, WHR WSR) of the study subjects were surveyed from self-recorded questionnaires and medical examination charts of the hospital. As a result, the serum lipid levels (TC, HDL-C, LDL-C, TG, AI) of the study subjects were increased significantly with higher level of obesity indices (WC, body fat rate, BMI, WHR WSR) in both sexes. The TC, LDL-C, TG, and AI showed that positive correlated with the WC, body fat rate, BMI, and WSR in both sexes, but HDL-C was negatively correlated with the WC, body fat rate, BMI, and WSR in both sexes. The above results suggest that the obesity indices and the serum lipid levels are closely related, i.e., the serum lipid levels increase with increasing obesity indices.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.1-6
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• 2022

In this paper, the research and development aim to strengthen the digital accessibility of the elderly by developing a kiosk incorporating AI voice recognition technology that can replace the promotional signage currently being installed and spread in the elderly and social welfare centers most frequently used by the digital underprivileged. It was intended to develop a converged system for the use of bulletin board functions, educational functions, and welfare center facilities, and to seek ways to increase the user's digital device experience through direct experience and education. Through interviews and surveys of senior citizens and social welfare centers, it was intended to collect problems and pain Points that the elderly currently experience in the process of using kiosks and apply them to the development process, and improve problems through pilot services. Through this study, it was confirmed that voice recognition technology is 2 to 6 times faster than keyboard input, so it is helpful for the elderly who are not familiar with device operation. However, it is necessary to improve the problem that there is a difference in the accuracy of the recognition rate according to the surrounding environment with noise. Through small efforts such as this study, we hope that the elderly will be a little free from digital alienation.

• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.27-34
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• 1997

This study was performed to offer the fundamental data for preventing and controlling the cardiovascular diseases of rural community women. The number of 168 women aged 40 to 50s who live in Kumsan-gun, Chungnam were selected for analysis after medical examination by a Kumsan-gun health center during the period of November to December in 1996. Total cholesterol(TC), triglyceride(TG), high density lipoprotein cholesterol(HDL-C), blood pressure(BP), degree of obesity(BMI) and atherosclerosis index(AI) were measured and relation between these physical measurements to serum lipid levels were studied. The results were as follows; 1. Mean TC level, TG level, BP, BMI and AI were significantly increased with advancing age. 2. Mean TC level, TG level, BMI and AI of borderline BP group and hypertension group were significantly increased than those of normal BP group. 3. Mean TC level, TG level and AI of obesity group were significantly increased than those of normal BMI group. 4. Simple correlation analysis showed that the positive correlation between TC, TG, BP, BMI and AI, but the level of HDL-C was negative correlation with TG and AI. 5. In multiple regression analysis taking HDL-C level as the dependent variable and TG, TC, BP, BMI, Age AI as the independent variable, AI, TG, BMI, TC were significantly related to HDL-C.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Journal of Yeungnam Medical Science
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• v.40 no.1
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• pp.109-111
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• 2023

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.274-276
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• 2021

As artificial intelligence develops rapidly, we can experience it in our everyday life such as with medical, education, and game applications. Traditional SW services were programmed explicitly by the intention of the programmer, and we have conducted evaluation on it. However, due to the uncertianty of AI services, risk follows to the products. Therefore, UX evaluations need to be different from traditional UX evaluations. Therefore, in this paper we suggest a AI-UX framework that consideres the task delegability, UX evaluations metrics, and individual differences.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2249-2254
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• 2013

The study was conducted to determine the effect of Malaysian jungle Tualang Honey (TH) on development of breast cancer induced by the carcinogen 7,12-dimethylbenz(${\alpha}$)anthracene (DMBA) in rats. Forty nulliparous female Sprague-Dawley rats were given 80 mg/kg DMBA then randomly divided into four groups: Group 1 served as a Control while Groups 2, 3 and 4 received 0.2, 1.0 or 2.0 g/kg bodyweight/day of TH, respectively, for 150 days. Results showed that breast cancers in the TH-treated groups had slower size increment and smaller mean tumor size (${\leq}2cm^3$) compared to Controls (${\leq}8cm^3$). The number of cancers developing in TH-treated groups was also significantly fewer (P<0.05). Histological grading showed majority of TH-treated group cancers to be of grade 1 and 2 compared to grade 3 in controls. There was an increasing trend of apoptotic index (AI) seen in TH-treated groups with increasing dosage of Tualang Honey, however, the mean AI values of all TH-treated groups were not significantly different from the Control value (p>0.05). In conclusion, Tualang Honey exerted positive modulation effects on DMBA-induced breast cancers in rats in this preliminary study.

• Clinical Endoscopy
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• v.57 no.1
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• pp.1-10
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• 2024

Barrett's esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC), and is caused by chronic gastroesophageal reflux. BE can progress over time from metaplasia to dysplasia, and eventually to EAC. EAC is associated with a poor prognosis, often due to advanced disease at the time of diagnosis. However, if BE is diagnosed early, pharmacologic and endoscopic treatments can prevent progression to EAC. The current standard of care for BE surveillance utilizes the Seattle protocol. Unfortunately, a sizable proportion of early EAC and BE-related high-grade dysplasia (HGD) are missed due to poor adherence to the Seattle protocol and sampling errors. New modalities using artificial intelligence (AI) have been proposed to improve the detection of early EAC and BE-related HGD. This review will focus on AI technology and its application to various endoscopic modalities such as high-definition white light endoscopy, narrow-band imaging, and volumetric laser endomicroscopy.