• Annual Conference of KIPS
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• 2023.11a
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• pp.424-425
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• 2023

AI 의료 영상 분석 기술은 의료 분야의 인력 부족 문제를 해결하는 방법으로 주목받고 있다. 이전 연구들은 세그멘테이션 라벨링과 질병 유무를 결합하여 판단하는데, 이 방법은 큰 비용과 시간이 소요된다. 본 논문은 의료 전문가의 세그멘테이션 라벨링 없이 병명 라벨만의 학습으로 질병을 어느 정도 진단할 수 있음을 보인다. 실험에 따르면 의미있는 결과를 확인할 수 있었다.

• Korean Journal of Veterinary Service
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• v.33 no.2
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• pp.113-119
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• 2010

Brucellosis is a major zoonosis caused by Gram negative facultative intracellular bacterial organisms of the genus Brucella that are pathogenic for a wide variety of animals and human beings. Because of its economic impact on animal health and the risk to the human population,most countries have a brucellosis control program. Brucellosis is also an economically important andprevalent disease in Bangladesh. The accurate and prompt diagnosis is very important in controlling and eradicating of the disease in animals. The present study was undertaken to determine the seroprevalence of brucellosis in cattle in Mymensingh and Patuakhali district of Bangladesh. A total of 120 serum samples were collected from the two districts along with a questionnaire related to the epidemiology of the disease. The sampleswere screened by using slow agglutination test and conformed by indirect enzyme linked immunosorbent assay. The overall seroprevalence of brucellosis in cattle was 5% and it was observed that, a higher prevalence of Brucella was found in female than male, through natural breeding than artificial insemination (AI) and animal above 4 years old are highly susceptible than younger ones. Higher prevalence was found in aborted animals in comparison with non aborted animal. Finally, the study revealed that the female animal has more susceptible to brucellosis and healthy semen should be used for AI.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• Journal of Oral Medicine and Pain
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• v.39 no.3
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• pp.100-106
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• 2014

Purpose: This study was performed to investigate the relationship between temporomandibular disorders (TMDs) and the asymmetry of the mandibular height. Methods: We compared 100 randomly selected TMD patients diagnosed by the research diagnostic criteria for TMD (RDC/TMD) Axis I with 100 non-TMD control subjects matched with the TMD patients in age and gender. The mandibular heights were measured on an orthopantomogram and the asymmetry index (AI) was calculated as previously described. Results: The absolute AI value of 4.37% turned out to be the least cut-off value defining asymmetry, which showed a significant difference in asymmetry incidence (p<0.01) between the TMD and control groups. The risk of TMD increased in the asymmetry group by 4.57 (odds ratio). The incidence of asymmetry was not related to age and gender in both of the TMD and control groups. When dividing the TMD group according to the RDC/TMD Axis I diagnosis, neither the incidence of muscle disorder nor disk displacement was related to the incidence of asymmetry. However, a higher incidence of asymmetry was observed in the subjects classified into the arthrosis/arthritis groups (p<0.01). Conclusions: Although it does not imply a direct cause-and-effect relationship, asymmetry resulting in more than 4.37% difference between mandibular heights may increase the risk of TMD and correlates positively to the incidence of arthritic change in the temporomandibular joint of TMD patients.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.172-178
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• 2021

The COVID-19 pandemic has been reshaping the world by accelerating non-contact services and technologies in various domains. Hospitals as a healthcare system lie at the center of the dramatic change because of their fundamental roles: medical diagnosis and treatments. Leading experts in health, science, and technologies have predicted that robotics and artificial intelligence (AI) can drive such a hospital transformation. Accordingly, several government-led projects have been developed and started toward smarter hospitals, where robots and AI replace or support healthcare personnel, particularly in the diagnosis and non-surgical treatment procedures. This article inspects the remaining element of healthcare services, i.e., surgical treatment, focusing on evaluating whether or not currently available laparoscopic surgical robotic systems are sufficiently preparing for the era of post-COVID-19 when contactless is the new normal. Challenges and future directions towards an effective, fully non-contact surgery are identified and summarized, including remote surgery assistance, domain-expansion of robotic surgery, and seamless integration with smart operating rooms, followed by emphasis on robot tranining for surgical staff.

• Korean Journal of Veterinary Service
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• v.31 no.3
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• pp.397-414
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• 2008

In order to evaluate conception rate of Hanwoo in northwestern region of Gyeongsang-nam-do, we investigated conception rate and reduction of reproductive disorder rate after artificial insemination (AI) in 1,000 heads of breeding cows, This study showed that 80.9% of cows were classified as fertility after 1st and 2nd AI. For a accurate pregnancy diagnosis with practicing ovariectomy and histeotomy, we comparatively investigated each of 80 slaughtered cows, including 30 of non-pregnancy, and used enzyme-linked immunosorbent assay (ELISA) for estimation of plasma progesterone concentration and serum luteal hormone. The mean diameter of non-pregnant corpus luteum is $18.9{\pm}4.2{\times}15.6{\pm}3.6 mm$ and that of pregnant corpus luteum is $22.5{\pm}2.7{\times}18.7{\pm}2.9 mm$. This indicates that corpus luteum is more developed in the ovary of pregnant than non-pregnant cows (P<0.05). The diameter of pregnant corpus luteum according to the stage of pregnancy showed $21.3{\pm}2.4{\pm}18.4{\pm}2.6 mm$ in early stage (1-3 month), $23.4{\pm}2.8{\times}19.1{\pm}2.7 mm$ in middle stage (4-6 month) and $22.8{\pm}3.0{\times}18.8{\pm}2.4mm$, in last stage (7-9 month). This indicates that corpus luteum in middle and last stage is more significantly developed than that of early stage(P<0.05). The mean plasma progesterone concentration of cows showing size of non-pregnant corpus luteum was $4.58{\pm}0.92ng/ml$ and that of pregnant corpus luteum $8.26{\pm}0.98ng/ml$. Thus, it was more significantly increased in pregnant corpus luteum(P<0.02).. However, it was low to $0.58{\pm}0.39ng/ml$. in estrus (corpus albicans). The plasma progesterone concentration according to gestation period was high in proportion to the degree of development in corpus luteum and more significantly increased (P<0.05) and maintained in middle and last state than early state. The concentration was sharply decreased to $0.56{\pm}0.32ng/ml$ at parturition. As a consequence, we can practice the early pregnancy diagnosis by confirming non-pregnancy when the mean plasma progesterone concentration is below 1ng/ml 19 to 22 days after AI and this can be available to diagnose reproductive disorder.

• Annual Conference of KIPS
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• 2024.10a
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• pp.461-462
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• 2024

In pediatric healthcare, early detection of cardiovascular diseases in newborns is crucial. Analyzing heart sounds using stethoscopes can be subjective and reliant on physician expertise, potentially leading to delayed diagnosis. There is a need for a simple method that can help even inexperienced doctors detect heart abnormalities without an electrocardiogram or ultrasound. Automated heart sound diagnosis systems can aid clinicians by providing accurate and early detection of abnormal heartbeats. To address this, we developed an intelligent deep-learning model incorporating CNN and LSTM to detect heart abnormalities based on artificial intelligence using heart sound data from stethoscope recordings. Our research achieved a high accuracy rate of 97.8%. Using audio data to introduce advanced models for cardiac abnormalities in children is essential for enhancing early detection and intervention in pediatric cardiovascular healthcare.

• Korean Journal of Clinical Pharmacy
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• v.34 no.2
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• pp.134-139
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• 2024

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clinical trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions-Drug, Biological, Dietary Supplement, or Combination Product-were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results: The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imaging or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to develop rapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.