• Tuberculosis and Respiratory Diseases
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• 제43권2호
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• pp.257-261
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• 1996

저자들은 10년간의 애성을 주소로 내원한 환자에서 전산화단층촬영과 기관지내시경을 통한 생검으로 확진된 기관 골형성증 1예를 경험하였기에 문헌고찰과 함께 보고하는 바이다.

• Tuberculosis and Respiratory Diseases
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• 제56권1호
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• pp.97-102
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• 2004

저자들은 객혈을 주소로 내원한 환자에서 조영증강 전산화 단층촬영과 혈관 조영술을 통해서 좌하엽으로의 비정상적인 체순환을 진단하였으며 좌하엽 폐절제술로 치료를 성공적으로 시행한 경험이 있기에 이를 보고하는 바이다.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.829-840
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• 2001

본 연구는 디지털 화상처리기술의 대표적인 응용분야로서 주목받고 있는 X선 사진을 대상으로 한 계산기 지원진단에 관한 연구의 일종으로서, 폐의 중요한 질환중 하나인 폐기종의 진단을 지원하는 계산기 시스템에 관한 연구이다. 구체적인 내용으로서는 흉부X선 사진으로부터 말초혈관을 자동추출하고, 추출된 혈관을 토대로 여러가지의 특징량을 구하여, 최종적으로 폐기종의 병세진행도를 정량평가하는 시스템에 관한 연구이다. 혈관 도형을 추출하여 병의 진행 정도를 정량적으로 평가하기 위해 본 연구에서 제안한 평가방법을 10장의 X선 사진에 설정된 189개의 관심영역에 적용하여, 의사의 평가치와 본 연구의 제안방법에 의한 평가치를 비교·검토함으로써 그 유효성을 검증하였다.

• 대한수의학회지
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• 제44권4호
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• pp.571-579
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• 2004

In order to obtain the genetic informations of the Korean isolates of porcine circovirus 2 (PCV2), nucleotide sequences of total genome of three isolates and open reading frame 2 (ORF2) of four isolates were determined and compared with those of other reference PCV2 isolates. Nucleotide sequences of 3 isolates showed over 99% homology with those of reference strain (GenBank accession no. AF027217). Point mutations were mainly determined on ORF2 regions but little on ORF1 regions. The patterns of pointmutated sites and nucleotide substitution on ORF2 regions were generally consistent between Korean isolates, and these mutated sites observed in Korean isolates were also relatively similar to those of foreign isolates. Phylogenetic analysis of nucleotide or amino acid sequences showed that there were minor branches consisting of three clusters; cluster of Korea, Canada and America, cluster of Spain and Taiwan, and the last cluster of French and China isolates. These results suggested that Korean PCV2s were probably originated from North America such as Canada or USA. The genetic informations obtained from this study could be useful for the research of diagnosis and pathogenecity of PCV2.

• The Plant Pathology Journal
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• 제35권2호
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• pp.164-171
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• 2019

An assay for detecting Apple scar skin viroid (ASSVd) was developed based on nucleic acid sequence based amplification (NASBA) in combination with realtime detection during the amplification process using molecular beacon. The ASSVd specific primers for amplification of the viroid RNA and molecular beacon for detecting the viroid were designed based on highly conserved regions of several ASSVd sequences including Korean isolate. The assay had a detection range of $1{\times}10^4$ to $1{\times}10^{12}$ ASSVd RNA $copies/{\mu}l$ with reproducibility and precision. Following the construction of standard curves based on time to positive (TTP) value for the serial dilutions ranging from $1{\times}10^7$ to $1{\times}10^{12}$ copies of the recombinant plasmid, a standard regression line was constructed by plotting the TTP values versus the logarithm of the starting ASSVd RNA copy number of 10-fold dilutions each. Compared to the established RT-PCR methods, our method was more sensitive for detecting ASSVd. The real-time quantitative NASBA method will be fast, sensitive, and reliable for routine diagnosis and selection of viroid-free stock materials. Furthermore, real-time quantitative NASBA may be especially useful for detecting low levels in apple trees with early viroid-infection stage and for monitoring the influence on tree growth.

• Journal of Yeungnam Medical Science
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• 제39권2호
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• pp.161-167
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• 2022

Congenital web formations are extremely rare anomalies of the extrahepatic biliary tree. We herein report a case of common bile duct septum combined with multiple intrahepatic bile duct strictures in a 74-year-old female patient who was successfully treated with radiological intervention. The patient initially visited the hospital because of upper abdominal pain. Imaging studies revealed multifocal strictures with dilatation in both intra- and extrahepatic ducts; the final clinical diagnosis was congenital common bile duct web combined with multiple intrahepatic duct strictures. Surgical treatment was not indicated because multiple biliary strictures were untreatable, and the disease was clinically diagnosed as benign. The multiple strictures were extensively dilated twice through bilateral percutaneous transhepatic biliary drainage (PTBD) for 2 months. After 1 month of observation, PTBD catheters were successfully removed. The patient is doing well at 6 months after completion of the radiological intervention, with the maintenance of normal liver function. Congenital web of the bile duct is very rare, and its treatment may vary depending on the patterns of biliary stenosis. In cases where surgical intervention is not indicated for congenital web and its associated disease, radiological intervention with balloon dilatation can be a viable therapeutic option.

• ETRI Journal
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• 제44권4호
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.815-816
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• 2009

자가 치유 시스템은 자율 컴퓨팅의 개념 중 하나로 사람의 개입 없이 시스템의 이상상태를 인식하고 정상상태로 복귀 가능한 시스템을 의미한다. 발생한 오류는 또 다른 오류를 유발할 수 있고, 하나 이상의 원인이 되는 오류나 사건이 있을 수 있다. 따라서 오류의 원인에 따른 치유전략을 필요로 하며 발생한 오류에서부터 전이될 수 있는 오류에 대한 추론을 요구하게 된다. 따라서 본 논문에서는 오류의 인과관계에 따른 진단 및 추론 기법을 제안하고자 한다. 제안사항을 통해 오류트리를 기반으로 하여 발생한 오류의 원인이 되는 오류를 파악할 수 있으며, 오류의 원인에 따른 치유 전략을 계획 가능하고, 발생 가능한 오류의 추론이 가능하다.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.53-63
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• 2023

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.