• 멤브레인
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• 제33권2호
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• pp.61-69
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• 2023

최근 COVID-19로 인해 증가한 급성 폐부전 중증환자 치료를 위한 인공폐 기술의 필요성이 부각되었다. 또한, 빠르게 진행되고 있는 인구고령화는 인공장기(artificial organ, AO) 기술에 대한 높은 수요를 불가피하게 만들고 있다. 분리막은폐, 신장, 간 및 췌장을 포함한 많은 AO 기기의 핵심 부품이다. 특히 인공폐(artificial lung, AL) 기술은 지난 50년간 빠르게 발전해왔지만, 장기부전 환자의 생존율은 50% 내외로 여전히 낮은 편이다. 현재 대부분의 AL 관련문헌은 임상결과에 집중되어 있으며, AL 분리막의 개발연구는 매우 부족한 편이다. 이에 대한 원인 중 하나는 AL 기술이 생명공학을 포함하여 고분자 화학 및 분리공정 기술을 아우르는 융합적 기술개발을 요구하기 때문인 것으로 판단된다. 본 총설에서는 헬스케어산업에서 AL 분리막 기술의 역할과 기술개발이 필요한 난제들을 정리하였다. 특히, 분리막 소재의 혈액적합성, 분리성능, 모듈 디자인 및 공정 구성 측면에서 다양한 연구개발이 필요하다는 부분을 강조하고자 한다.

• 멤브레인
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• 제14권1호
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• pp.18-25
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• 2004

본 연구의 목적은 정맥 내 폐 보조장치 설계에 있어서 기체전달을 예측하기 위하여 혈관 내 폐 보조장치를 정맥 내에 삽입하기 전, 여러 가지 설계조건에서 실험적 모델링을 통하여 예측식을 찾고자 시도하였다. 실험결과 중공사 충진율의 함수에 따라 기체전달을 예측할 수 있었다. 실험에 의하여 얻어진 기체전달은 예측식에 의한 기체전달과 유사하여, 식에 대한 신뢰성을 얻을 수 있었다. 그러므로, 중공사의 충진율의 함수에 따라 기체전달을 예측할 수 있으며, 혈관 내 폐 보조장치의 기체전달을 예측하는데 매우 유용하였다.

• Journal of Mechanical Science and Technology
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• 제16권11호
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• pp.1470-1476
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• 2002

The goal of this work is to design and build an implantable artificial lung that can be inserted as a whole into a large vein in the body with the least effect on cardiovascular hemodynamics. The experimental results demonstrate that the pressure drop is not entirely related to viscosity effects. The friction factor decreases with an increase in the number of tied-hollow fibers at a constant Reynolds number A uniform flow pattern without stagnation is observed at all numbers of tied hollow fibers tested. The tied hollow fiber module, built in this study with 3 cm of outer diameter of module. 380 m of outer diameter of tied hollow fiber, and 700 number of tied hollow fiber with length of 60 cm, which shows a pressure drop of 13-16 mmHg, satisfies the required pressure drop qualifying 15 mmHg as an intravascular artificial lung.

• 한국인공지능학회지
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• 제7권2호
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• Asian Pacific Journal of Cancer Prevention
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• 제15권13호
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• pp.5349-5353
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• 2014

Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (${\geq}22days$, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (${\geq}61days$ old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors. The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

• Asian Pacific Journal of Cancer Prevention
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• 제15권23호
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.1614-1615
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• 2011

It is difficult to differentially diagnose between lung cancer and benign inflammatory lung lesion due to high false positive rate on F-18 FDG-PET. We investigated whether application of artificial neural network to this diagnosis may be helpful. We reviewed the medical records and F-18 FDG PET images of 12 patients, selecting clinical and PET variables such as SUV. For selected variables and confirm, multilayer neural perceptron was applied in crossvalidation method and compared to visual interpretation. Neural network correctly classified the lung lesions in 83%, and reduced greately the false positive rate. However, false negative rate was not influenced. Application of neural network to the differential diagnosis between lung cancer and benigh inflammatory lesion may be helpful. Further studies with more patients are warranted.

• Journal of Chest Surgery
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• 제22권2호
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• pp.202-211
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• 1989

We experienced a series of animal experimental studies of the total artificial heart in 1988. So called, "Korean Heart* was used in this study, which is developed and fabricated in the Department of Biomedical Engineering, College of Med., S.N.U.. "Korean Heart" is a Rolling-Cylinder Motor-Driven type which is a newly developed electromechanical heart over the shortcomes of the previous artificial hearts, especially pneumatic type. The advantages of the "Korean Heart" are total implantability, quiet and smooth movement, small size fittable in oriental people, etc. The animal experiments were performed two times, as an assist device in sheep and total artificial heart implant experiment in calf weighing 100 kg. After total implantation, the artificial heart was well functioned in movement and hemodynamic control. So that, the calf was recovered excellently, which was able to stand up by herself and take an oral intake. Total survival time was 100 hours and the cause of death was a sudden pumping failure [electrical connection problem]. Several postoperative laboratory results almost within normal limits and no hemolysis, but in autopsy, the multiple thromboembolic findings were seen at the lung and kidney.n at the lung and kidney.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.537-540
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• 2021

One of the most promising technologies that is raised from the fourth industrial revolution is Digital Twin (DT). A DT captures attributes and behaviors of the entity suitable for communication, storage, interpretation or processing within certain context. A digital twin based on microservice framework architecture is proposed in this paper which identifies elements required for the complete orchestration of microservice based Survival Analysis of Lung Cancer Patients. Integration of microservices and Digital Twin Technology is studied.

• 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.