• Radiation Oncology Journal
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• 제32권3호
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• pp.116-124
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• 2014

Purpose: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. Materials and Methods: Sixty-seven treatment-$na{\ddot{i}}ve$ HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. Results: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. Conclusion: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

• The Korean Journal of Physiology and Pharmacology
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• 제24권1호
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• 대한치과보철학회지
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• 제55권4호
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• pp.410-418
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• 2017

법랑질 형성 부전증은 유전적인 결함으로 인해 구조적으로 약한 법랑질이 형성되는 질환이다. 이들 환자들은 이른 나이부터 진행되는 법랑질 마모에 의한 시린 증상과 비심미적인 치아를 주소로 치과에 내원하게 되며, 성장기 이후에는 전악 보철 수복을 통해 치아의 기능성과 심미성을 회복해 주게 된다. 법랑질 형성 부전증 환자에서 보여지는 전치부 개방 교합은, 구치의 교합면 마모 및 보상성 맹출에 의한 수직적 수복 공간 문제와 결부되어 보철 치료를 어렵게 하는 요인이 된다. 따라서 전치 길이의 결정 및 교합 고경의 거상 여부, 전방유도의 설정은 신중히 결정되어야 한다. 근래에는 Computer aided design-computer aided manufacturing (CAD/CAM) 기술을 이용하여 진단 및 최종 수복으로의 이행이 용이해 졌다. 본 증례에서는 전치부 개방교합을 가지고 있는 법랑질 형성 부전증 환자에서, CAD/CAM을 이용한 전악 수복을 시행한 후, 양호한 경과를 보이고 있기에 이를 보고하고자 한다.

• 대한치과보철학회지
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• 제57권2호
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• pp.160-170
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• 2019

상악 전치부와 같은 기능 및 심미성의 개선이 조화롭게 요구되는 부위의 치료 시에는 다른 부위보다 더 많은 지식과 기술을 필요로 한다. 특히 전방 유도(anterior guidance)를 결정하는 상악 전치부 설면 외형을 제대로 형성하지 못하면, 기능적인 불편감과 함께 전체 치열의 불안정성을 야기한다. 적절한 원리를 이용하여 전방유도를 설정한 후 임시 수복물 제작 및 조정을 통해 조화로운 전방 유도를 확보했다면 임시 수복물의 설면 외형을 최종 보철물로 정확하게 재현하는 방법에 대해 주의 깊게 고려해야 할 필요가 있다. 본 증례에서는 체계적인 진단 및 치료를 위하여 수정된 Dahl 원리(modified Dahl principle) 및 computer-aided design/computer-aided manufacturing (CAD/CAM) 시스템의 복제 기법(copy-milled)을 이용하여 적절한 전방 유도를 설정하고, 지대치의 디지털 이미지와 임시 수복물의 디지털 이미지를 중첩시켜 보다 정확하게 보철물 형태를 재현하고자 하였다. 이에 기능적, 심미적 개선을 도모하여 환자와 술자 모두에게 만족스러운 치료결과 및 예후를 얻었기에 보고하는 바이다.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.9-16
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• 2016

In this paper, we present a performance evaluation agent based on fuzzy cluster analysis and validity measures. The proposed agent is consists of three modules, fuzzy cluster analyzer, performance evaluation measures, and feature ranking algorithm for feature selection step in CAD system. Feature selection is an important step commonly used to create more accurate system to help human experts. Through this agent, we get the feature ranking on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. Also we design a CAD system incorporating the agent and apply five different feature combinations to the system. Experimental results proposed approach has higher classification accuracy and shows the feasibility as a diagnosis supporting tool.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• 대한유전성대사질환학회지
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• 제6권1호
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• pp.40-51
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• 2006

Purpose: A personal computer-based system was developed for automated metabolic profiling of organic aciduria and aminoacidopathy by gas chromatography-mass spectrometry and data interpretation for the diagnosis of metabolic disorders Methods: For automatic data profiling and interpretation, we compiled retention time, two target ions and their intensity ratio for 77 organic acids and 13 amino acids metabolites. Metabolites above the cut-off values were flagged as abnormal compounds. The data interpretation was a based on combination of flagged metabolites. Diagnostic or index metabolites were categorized into three groups, "and", "or" and "NO" compiled for each disorder to improve the specificity of the diagnosis. Groups "and" and "or" comprised essential and optional compounds, respectively, to reach a specific diagnosis. Group "NO" comprised metabolites that must be absent to make a definite diagnosis. We tested this system by analyzing patients with confirmed Propionic aciduria and others. Results: In all cases, the diagnostic metabolites were identified and correct diagnosis was founded to be made among the possible disease suggested by the system. Conclusion: The study showed that the developed method could be the method of choices in rapid, sensitive and simultaneous screening for organic aciduria and amino acidopathy with this simplified automated system.

• 대한방사선기술학회지:방사선기술과학
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• 제40권2호
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• pp.237-243
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• 2017

본 연구는 갑상샘 초음파 영상에서 정상 및 악성결절의 세포병리 진단결과를 바탕으로 GLCM 알고리즘분석을 통한 후향적 연구를 시행하여 컴퓨터보조진단의 적용 가능성을 평가하였다. GLCM 알고리즘의 6가지 파라미터를 이용한 갑상샘 악성결절의 인식률 평가와 ROC 곡선을 분석하였다. 실험 결과는 에너지 97%, 대조도 93%, 상관관계 92%, 동질성 92%, 엔트로피 100%, 분산 100%의 높은 질환인식률을 나타내었다. ROC 곡선 분석에서 각 파라미터의 곡선아래면적이 0.947(p=0.001) 이상을 나타내어 갑상샘 악성결절의 인식에 의미가 있는 결과로 나타났다. 또한 GLCM에서 각 파라미터의 cut-off값 결정으로 정량적인 컴퓨터보조진단의 분석을 통한 질환예측이 가능할 것으로 판단된다.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Nuclear Medicine and Molecular Imaging
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• 제41권3호
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• pp.201-208
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• 2007

목적: 유방암의 감별진단에서 기존의 유방 초음파 검사나 핵의학 유방SPECT의 진단성능에는 한계가 있다. 저자들은 초음파 컴퓨터진단시스템(CAD: computer aided diagnosis)의 적용에 의하여 유방 SPECT의 진단성능이 향상되는지를 알아보았다. 대상 및 방법: 유방초음파 및 유방 SPECT(Tc-99m tetrofosmin)를 시행하고 수술후 확진된 여자환자 40명(21명:악성종양, 19명:양성병변)의 영상자료를 분석하였다. 유방초음파영상을 컴퓨터분석 소프트웨어를 이용하여 병변의 경계를 분리한 후, 영상의 형태학적 특성들을 추출하였다. 초음파영상에서 추출된 형태학적 특성 중에서 감별능력이 있는 것으로 판단된 특성들을 골라 정량화하였다. 정량화된 형태학적 특성값들을 유방SPECT에서 구한 병변 대 반대측 유방의 방사능비와 판별분석에 의하여 결합하여 새로운 파라메터인 D-수치를 산출하였다. 유방SPECT의 병변 방사능비, 유방초음파 컴퓨터진단시스템의 악성확률 및 두가지를 결합한 D-수치에 대하여 수신자판단특성곡선(ROC curve) 분석을 이용하여 최적 판별 수치(cut-off value)를 구하고 이에 의한 유방암 진단의 예민도, 특이도 및 정확도를 계산하여 유방 SPECT과 초음파 컴퓨터진단시스템의 결합에 의한 진단성능을 기존의 유방 SPECT의 진단성능과 비교하였다. 결과: ROC curve분석상에서 유방암 진단에 대한 성능은 유방초음파의 컴퓨터 분석시스템 및 유방SPECT 각각 모두 우수하였다(area under curve=0.831 and 0.846). 두 결과를 통계적인 방법으로 결합하였을 때 ROC curve분석의 area under curve는(0.860) 향상되었으나, 최적 판별 수치(cut-off value)에 의한 유방암 진단의 예민도, 특이도 및 정확도에는 통계적인 차이는 없었다. 결론: 유방초음파의 컴퓨터분석시스템의 결과를 유방 SPECT에 적용하여 유방암의 진단성능을 향상시킬 수 있었지만 통계적으로는 유의하지 못하였다. 향후 추가적인 연구가 필요할 것으로 보인다.