• Journal of Oral Medicine and Pain
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• 제39권3호
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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.

• 한국동물위생학회지
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• 제33권2호
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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.

• 로봇학회논문지
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• 제16권2호
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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.

• 한국임상약학회지
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• 제34권2호
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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.

• 대한치과교정학회지
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• 제52권2호
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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.

• 한국동물위생학회지
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• 제31권3호
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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.

• Journal of Preventive Medicine and Public Health
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• 제22권1호
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• pp.57-64
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• 1989

A decision support system (DSS) was developed to support doctor's decision-making in diagnosing hearing loss. The final diagnosis encompassed 41 diseases with the problem of hearing loss. The system was developed by integrating model-oriented DSS technique and artificial intelligence technology. The system can be used as both diagnosis tool and teaching tool for medical students. Furthermore, the AI technology obtained from this study may also be used in developing DSS for hospital management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• 식물병연구
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• 제30권1호
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• 실천공학교육논문지
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• 제15권3호
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• pp.605-614
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• 2023

디지털 대전환 시대를 맞아 개인 맞춤형 교육을 실현하기 위해 교육 분야에서 인공지능 기반 학습 서비스들이 등장하고 있다. 본 연구에서는 인공지능 기반 학습 서비스를 학교 현장에 적용하기 위한 개발 방향을 살펴보고자 하였다. 인공지능 기반 수학학습 서비스로 아이스크림에듀에서 개발한 '수학의 세포들'을 선택하여 교수자 관점에서 기능별 요구를 조사하였다. 그 결과를 IPA를 활용하여 중요도와 적합도로 분석하면서 전문가 의견을 조사하여 서비스의 구체적인 개발 방향을 탐색하였다. 연구결과, 진단, 학습, 평가, 관리 등 모든 영역에서의 중요도는 평균 4.82, 적합도는 평균 4.56로 대부분의 문항에서 우수한 결과가 나타났으며, 특히 중요도가 적합도보다 높게 나타났다. 세부적인 일부 기능 중 개념 학습, 맞춤형 과제 제시, 평가 결과 분석 기능, 대시보드 관련 기능과 대시보드 내 학습 자료가 학생들이 이해하기에 직관적이지 않아 보완이 필요하다는 의견을 확인하였다. 본 연구는 교수자의 관점에서 인공지능 기반 수학학습 서비스에 대한 요구 및 전문가 의견을 정리하여 '수학의 세포들'의 방향을 탐색하는데 유의미한 정보를 제공하였다는 의의가 있다.