• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.27-35
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• 2009

A paradigm of medical industry is changing quickly to u-healthcare according to entry toward an aging society and improvement of quality of life(QoL). The change toward u-healthcare is meaningful since meaning of healthcare is redefined by prevention and management instead of medical service such as diagnosis of disease and treatment. However, the interest about u-healthcare is only concentrated to derivation of new healthcare service, development of medical measurement appliances(Sensors), and integration and standardization of medical information. Therefore, in this paper, the main ai of this study is trying to realize and implement u-healthcare technology through primary philosophies of ubiquitous composition such as Disappear Computing, Invisible Computing, and Calm Computing and development of user-centered technology.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.1-9
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• 2021

Children's drawings are widely used in the diagnosis of children's psychology as a means of expressing inner feelings. This paper proposes a children's drawings-based object detection algorithm applicable to children's psychology analysis. First, the sketch area from the picture was extracted and the data labeling process was also performed. Then, we trained and evaluated a Faster R-CNN based object detection model using the labeled datasets. Based on the detection results, information about the drawing's area, position, or color histogram is calculated to analyze primitive information about the drawings quickly and easily. The results of this paper show that Artificial Intelligence-based object detection algorithms were helpful in terms of psychological analysis using children's drawings.

• Clinical and Experimental Pediatrics
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• v.62 no.5
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• pp.187-192
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• 2019

Purpose: The importance of the neurodevelopmental outcomes of very-low-birth-weight (VLBW) infants has been emphasized as their mortality rate has markedly improved. This study aimed to assess the validity of the Korean Developmental Screening Test (K-DST), a developmental screening tool approved by the Korean Society of Pediatrics, for the timely diagnosis of neurodevelopmental delay in VLBW infants. Methods: Subjects included VLBW infants enrolled in the Korean Neonatal Network database between January 2012 and December 2014. The collected data were analyzed for sensitivity, specificity, positive predictive value, and negative predictive value (NPV) in the K-DST compared to those in the Bayley Scales of Infant Development-II for VLBW infants. Results: A total of 173 patients were enrolled. Their mean gestational age and mean birth weight were $27.5{\pm}2.8weeks$ and $980.5{\pm}272.1g$, respectively. The frequency of failed psychomotor developmental index (PDI) <85 was similar to that in at least one domain of K-DST <1 standard deviation. Failure in more than one K-DST domain compared with a mental developmental index (MDI) <85 showed a sensitivity and NPV of 73.2% and 75.0%, respectively. Failure in more than one K-DST domain compared with PDI <85 showed a sensitivity and NPV of 60.3% and 71.6%, respectively. Each K-DST domain had a stronger correlation with predicting a failing MDI <85 than a failing PDI <85 (P<0.05). Conclusion: K-DST could be a useful screening tool for predicting mental developmental delay in VLBW infants and referring them for neurodevelopmental assessments.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.1-7
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• 2023

Recently, it is increasing that a issue of concern about radiation exposure. It affects soil, water, air, crops, etc., and in the long term, environmental pollution and food pollution occur, and it is considered to cause social problems and economic damage. Radiation exposure causes diseases and health problems, but as a method for diagnosing diseases, nuclear medicine tests such as X-ray imaging, CT, and PET-CT are conducted, and radiation isotopes are exposed for the purpose of cancer treatment. A Hungarian case study on radiation in water, particularly drinking water, following the release of radioactive waste from Fukushima, and an examination of the Larsemann Hills area in Antarctica, found that it was within the prescribed radioactivity limits of drinking water recommended by the World Health Organization. We looked at radioprotective agents, focusing on DNA damage, cell and organ damage, and cancer, and also investigated various literatures on ACE inhibitors, antioxidants, and natural substances among restoration materials. Although exposed to radiation in everyday life, the reason why it can be safe is probably because there is a radiation protection material and a recovery material for radiation exposure, so we are trying to find possible materials.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.150-156
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• 2023

For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.333-335
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• 2022

본 논문에서는 기존에 두 가지 모델이 필요했던 작업을 하나의 모델로 처리할 수 있는 자연어 처리 아키텍처를 제안한다. 단일 모델로 알츠하이머 환자의 언어패턴과 대화맥락을 분석하고 두 가지 결과인 환자분류와 챗봇의 대답을 도출한다. 일상생활에서 챗봇으로 환자의 언어특징을 파악한다면 의사는 조기진단을 위해 더 정밀한 진단과 치료를 계획할 수 있다. 제안된 모델은 전문가가 필요했던 질문지법을 대체하는 챗봇 개발에 활용된다. 모델이 수행하는 자연어 처리 작업은 두 가지이다. 첫 번째는 환자가 병을 가졌는지 여부를 확률로 표시하는 '자연어 분류'이고 두 번째는 환자의 대답에 대한 챗봇의 다음 '대답을 생성'하는 것이다. 전반부에서는 셀프어탠션 신경망을 통해 환자 발화 특징인 맥락벡터(context vector)를 추출한다. 이 맥락벡터와 챗봇(전문가, 진행자)의 질문을 함께 인코더에 입력해 질문자와 환자 사이 상호작용 특징을 담은 행렬을 얻는다. 벡터화된 행렬은 환자분류를 위한 확률값이 된다. 행렬을 챗봇(진행자)의 다음 대답과 함께 디코더에 입력해 다음 발화를 생성한다. 이 구조를 DementiaBank의 쿠키도둑묘사 말뭉치로 학습한 결과 인코더와 디코더의 손실함수 값이 유의미하게 줄어들며 수렴하는 양상을 확인할 수 있었다. 이는 알츠하이머병 환자의 발화 언어패턴을 포착하는 것이 향후 해당 병의 조기진단과 종단연구에 기여할 수 있음을 보여준다.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.1
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• pp.31-42
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• 2024

Objective : In order to understand the changes in domestic approval regulations applicable to traditional Korean medical device companies, this article will explain the major amendments 「Regulation on Medical Device Approval Report Review, etc.」 from 2005 to the present on a year-by-year basis, and provide a counter plan to the recent changes in approval regulations. Methods : We analysed the changes in approval regulatory amendments related to the traditional Korean medical devices from 2005 to the present. Results : The Ministry of Food and Drug Safety is continuously improving medical device approval regulations to ensure the global competitiveness of domestic medical devices and contribute to the improvement of public health. Recent major approval regulatory amendments include the establishment of a review system for software medical devices and digital therapeutics, the recognition of real world evidence materials, the introduction of a biological evaluation of medical devices within a risk management process and a medical device approval licence renewal system. Conclusions : It is expected that the range of medical devices available to Korean medicine doctors will continue to expand in the future through the provision of non-face-to-face medical services and the development of advanced and new medical devices, as well as wearable medical devices and digital therapeutics. In order to increase the market entry potential of traditional Korean medical devices that incorporate advanced technologies such as digital technology and AI-based diagnosis and prediction technology, it is urgent that the government provide significant support to traditional Korean medical device companies to improve approval regulatory compliance.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Journal of Life Science
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• v.19 no.9
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• pp.1328-1332
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• 2009

Anisakidosis is caused by anisakid nematodes (family Anisakidae) larvae which can cause not only direct tissue damage but also a severe allergic response related to excretory-secretion products. Lots of different species of anisakid larvae, including Anisakis simplex, Contracaecum, Goezia, Pseudoterranova, and Hysterothylacium, cause the anisakidosis. But it is difficult to diagnosis the species of larvae since the morphologies of larval anisakid nematodes are almost indistinguishable. In order to diagnosis the differential infections of larval anisakid nematodes, polymerase chain reaction - restriction fragment length polymorphism (PCR-RFLP) of 18S rDNA - was conducted. Three major species of anisakid larvae including A. simplex, C.ontracaecum spp, and Goezia spp. were collected from mackerel (Scomber japonicus), mullet (Mugil cephalus), founder (Paralichthys olivaceus), eel (Astroconger myriaster) and red sea bream (Pagrus major). PCR amplified 18S rDNA from each species of anisakid larvae was digested with eight restriction enzymes including Taq I, Hinf I, Hha I, Alu I, Dde I, Hae III, Sau96 I, and Sau3A I. The original sizes of PCR amplified 18S rDNA were 2.0Kb in both anisakid larvaes and Goezia. Restrction enzymes including Hinf 1, Alu 1, Hha I, Dde 1 and Hae III cut differently and distinguished the A. simplex and Contracaecum type C'. However, Contracaecum type A showed two different restriction enzyme cutting patterns by Taq 1, Hinf I, Alu 1, and Dde 1. One of the patterns was the same as those of A. simplex, Contracaecum type C' and Goezia and the other was unique. These results suggest that PCR-RFLP pattern by Hinf 1, Alu 1, Hae I, Dde 1 and Hae III can be applied to differential diagnosis of human infection with A. simplex and Contracaecum type C'. Contracaecum type A needs further study of classification by morphological characteristics and genetic analysis.