• Journal of Information Processing Systems
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• 제12권1호
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• 대한의료기공학회지
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• 제11권1호
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• pp.198-220
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• 2009

1. Korean archery is very important in body-mind therapy because it has excellent effect in physical and spiritual culture. 2. Korean archery culture has physical effect that reinforce eum-gyeong(陰經) muscles, thus it much affect on reinforcement of ha-cho(下焦) and ha-dan-jeon(下丹田). 3. Korea archery has spiritual effect : etiquette, concentration, a state of balance, discipline, self-control, modesty, courage. 4. Author present model of korean archery culture and think enforcement the model will affect therapy and prevention Korean archery therapy is on elementary level, lack clinical datas. If the data of therapy which is proved objectively is more larger, korean archery therapy will be more popular by exercise therapies.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.454-456
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• 2021

본 논문은 웹의 발달로 인하여 의료 서비스들이 기존의 Client-Server 방식의 제품에서 Web 방식의 제품으로 변경되고 있는 현대 흐름에서 인공지능 어플리케이션 또한 Web 으로 서비스 하기 위한 방법과 구현된 요로결석 AI 어플리케이션에 대해 기술한다. 이를 구현하기 위해 Python 기반의 Flask 라는 마이크로 웹 프레임워크를 사용하여 DICOM 핸들링, Pre-Processing, Mask 를 생성하고 Predict 결과를 Model Serving 을 통하여 Urinary Stone Segmentation Model 이 서비스되는 인공지능 웹 어플리케이션 동작 방식과 수행 결과를 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4174-4190
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• 2019

IEC 62304 is a standard for the medical device software lifecycle. Developers must develop software that complies with all specifications in the standard for licensing. However, because the standard contains not only a large number of specifications, but also domain-specific information and association relationships between specifications, it requires considerable effort and time for developers to understand and interpret the standard. To support developers, this paper presents a method for extracting the contents of the IEC 62304 standard as a goal model, which is the core methodologies of requirements engineering. The proposed method analyzes the grammar of the standard to robustly extract complex structures and various information from standard specifications and define rules that extract goals and links from syntactic element units. We validated the actual extraction process for the standard document experimentally. Based on the extracted goal model, developers can intuitively and efficiently comply with the standard and track specific information within the medical software and standard domains.

• Korean Journal of Radiology
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• 제24권6호
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• Tuberculosis and Respiratory Diseases
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• 제72권3호
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• pp.302-309
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• 2012

Background: Korean regression models for spirometric reference values are different from those of Americans. Using spirometry results of Korean adults, goodness-of-fits of the Korean and the USA Caucasian regression models for forced vital capacity (FVC) and forced expiratory volume in one second ($FEV_1$) were compared. Methods: The number of study participants was 2,360 (1,124 males and 1,236 females). Spirometry was performed under the guidelines of the American Thoracic Society and the European Respiratory Society. After excluding unsuitable participants, spirometric data for 729 individuals (105 males and 624 females) was included in the statistical analysis. The estimated FVC and $FEV_1$ values were compared with those measured. Goodness-of-fits for Korean and USA Caucasian models were compared using an F-test. Results: In males, the expected values of FVC and $FEV_1$ using the Korean model were 12.5% and 5.7% greater than those measured, respectively. The corresponding values for the USA Caucasian model were 3.5% and 0.6%. In females, the difference in FVC and $FEV_1$ were 13.5% and 7.7% for the Korean model, and 6.3% and 0.4% for the USA model, respectively. Goodness-of-fit for the Korean model regarding FVC was not good to the study population, but the Korean regression model for $FEV_1$, and the USA Caucasian models for FVC and $FEV_1$ showed good fits to the measured data. Conclusion: These results suggest that the USA Caucasian model correlates better to the measured data than the Korean model. Using reference values derived from the Korean model can lead to an overestimation regarding the prevalence of abnormal lung function.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5095-5099
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• 2015

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

• Journal of Photoscience
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• 제9권2호
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• pp.494-496
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• 2002

Fluorouracil (FU) is well known to induce discoid lupus-like eruption at the sun exposure sites in Japan. It means the associations of UVB with drug induced DLE. It is still obscure which cytokines are involved in the development of DLE. To address the issue, we established a murine model of FU and UVB-induced discoid lupus and could show the Th1 dominant cytokine profiles in DLE model of TCR $\alpha$ chain KO mice treated with FU and UVB.

• Natural Product Sciences
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• 제16권3호
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• pp.159-163
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• 2010

A mixture of several detoxified bacterial strains ($Sterodin^{(R)}$) has been studied for anti-inflammatory effect in Wistar rats on carrageenin, dextran and prostaglandin $E_1$ ($PGE_1$) induced edema in acute model and cotton pellet and carrageenin induced sub-acute model, while, Freund's adjuvant induced chronic model. The bacterial strains showed strong inhibitory activity in acute, sub-acute and chronic models of inflammation. Further, it reduced ${\alpha}1$ acid glycoprotein and ${\alpha}2$ macroglobulin levels in serum and prostaglandin $E_2$ in inflamed paw. These results indicated that the bacterial strains probably act through prostaglandin mediatory pathways and may be useful in treatment of inflammation.

• 전기학회논문지
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• 제65권12호
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• pp.2019-2022
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• 2016

In the presence of an electrically conducting medical lead, radio frequency (RF) coils in magnetic resonance imaging (MRI) systems may concentrate the RF energy and cause tissue heating near the lead. A novel design for a medical lead to reduce this heating by introducing pins in the lead is presented. Peak 10 g specific absorption rate (SAR) in heart tissue, an indicator of heating, was calculated and compared for both conventional (Medtronic) lead design and our proposed design. Remcom XFdtd software was used to calculate the peak SAR distribution in a realistic model of the human body. The model contained a medical lead that was exposed to RF magnetic fields at 64 MHz (1.5 T), 128 MHz (3 T) and 300 MHz (7 T) using a model of an MR birdcage body coil. The proposed design of adding pins to the medical lead can significantly reduce the heating from different MRI systems.