• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Journal of Sasang Constitutional Medicine
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• v.26 no.1
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• pp.45-54
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• 2014

Objectives This research was proposed to present Clinical Practice Guideline(CPG) for Soeumin Disease of Sasang Constitutional Medicine(SCM): Greater Yin Symptomatology. This CPG was developed by the national-wide experts committee consisting of SCM professors. Methods First, it was performed that search and collection of literature related SCM such as "Dongeuisusebowon", Textbook of SCM, Clinical Guidebook of SCM and Fundamental research to standardize diagnosis of Sasang Constitutional Medicine. And journal search related clinical trial or Human complementary medicine of SCM was performed domestic and overseas. Finally, 1 article was selected and included in CPG for Greater Yin Symptomatology of Stomach Cold-based Interior Cold disease in Soeumin disease. Results & Conclusions CPG of Greater Yin symptomatology in Soeumin Disease include classification, definition and standard symptoms of each pattern. Greater Yin symptomatology is classified into mild and moderate pattern by severity. Greater Yin Symptomatology Mild pattern is classified into Greater Yin Symptomatology accompanied abdominal pain and bowel irritability and Greater Yin pattern accompanied Epigastric stuffiness and fullness. And Greater Yin Symptomatology moderate pattern is classified into Greater Yin pattern accompanied Jaundice, Greater Yin pattern accompanied Edema and Greater Yin pattern by Yin toxin.

• Journal of Sasang Constitutional Medicine
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• v.26 no.1
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• pp.37-44
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• 2014

Objectives This research was proposed to present Clinical Practice Guideline(CPG) for Soeumin Disease of Sasang Constitutional Medicine(SCM): Yang Depletion Symptomatology. Methods This CPG was developed by the national-wide experts committee consisting of the society of Sasang Constitutional Medicine. it was performed by search and collection of literature related SCM, opinion of SCM experts and journal search. And it was followed by CPG's guideline. Results & Conclusions No article was selected and included in CPG for Yang Depletion Symptomatology of Kidney Heat-based Exterior Heat disease in Soeumin disease. CPG of Yang Depletion symptomatology in Soeumin Disease include classification, definition and standard symptoms of each pattern. Yang Depletion symptomatology is classified into severe and critical pattern by severity. Yang Depletion Symptomatology severe pattern is classified into initial phase pattern and intermediate phase pattern. And Yang Depletion Symptomatology critical pattern is classified into advanced phase pattern.

• Journal of Periodontal and Implant Science
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• v.46 no.3
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• pp.136-151
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• 2016

Peri-implant disease is a serious problem that plagues today's dentistry, both in terms of therapy and epidemiology. With the expansion of the practice of implantology and an increasing number of implants placed annually, the frequency of peri-implant disease has greatly expanded. Its clinical manifestations, in the absence of a globally established classification, are peri-implant mucositis and peri-implantitis, the counterparts of gingivitis and periodontitis, respectively. However, many doubts remain about its features. Official diagnostic criteria, globally recognized by the dental community, have not yet been introduced. The latest studies using metagenomic methods are casting doubt on the assumption of microbial equivalence between periodontal and peri-implant crevices. Research on most of the features of peri-implant disease remains at an early stage; moreover, there is not a commonly accepted treatment for it. In any case, although the evidence so far collected is limited, we need to be aware of the current state of the science regarding this topic to better understand and ultimately prevent this disease.

• Tuberculosis and Respiratory Diseases
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• v.83 no.3
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• pp.195-200
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• 2020

The disease concept of interstitial lung disease with idiopathic pulmonary fibrosis at its core has been relied on for many years depending on morphological classification. The separation of non-specific interstitial pneumonia with a relatively good prognosis from usual interstitial pneumonia is also based on the perception that morphology enables predict the prognosis. Beginning with dust-exposed lungs, initially, interstitial pneumonia is classified by anatomical pathology. Diagnostic imaging has dramatically improved the diagnostic technology for surviving patients through the introduction of high-resolution computed tomography scan. And now, with the introduction of therapeutics, the direction of diagnosis is turning. It can be broadly classified into to make known the importance of early diagnosis, and to understand the importance of predicting the speed of progression/deterioration of pathological conditions. For this reason, the insight of "early lesions" has been discussed. There are reports that the presence or absence of interstitial lung abnormalities affects the prognosis. Searching for a biomarker is another prognostic indicator search. However, as is the case with many chronic diseases, pathological conditions that progress linearly are extremely rare. Rather, it progresses while changing in response to environmental factors. In interstitial lung disease, deterioration of respiratory functions most closely reflect prognosis. Treatment is determined by combining dynamic indicators as faithful indicators of restrictive impairments. Reconsidering the history being classified under the disease concept, the need to reorganize treatment targets based on common pathological phenotype is under discussed. What is the disease concept? That aspect changes with the discussion of improving prognosis.

• The Korean Journal of Pain
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• v.33 no.1
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• pp.90-96
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• 2020

Background: Parkinson's disease (PD) is a neurodegenerative disorder that is the second most common disorder after Alzheimer's disease. PD includes both "motor" and "non-motor" symptoms, one of which is pain. The aim of this study was to investigate the clinical characteristics of pain in patients with PD. Methods: This cross-sectional study included 250 patients diagnosed with PD, 70% of which had mild to moderate PD (stages 2/3 of Hoehn and Yahr scale). The average age was 67.4 years, and the average duration since PD diagnosis was 7.1 years. Relevant data collected from PD patients were obtained from their personal medical history. Results: The prevalence of pain was found to be high (82%), with most patients (79.2%) relating their pain to PD. Disease duration was correlated with the frequency of intense pain (R: 0.393; P < 0.05). PD pain is most frequently perceived as an electrical current (64%), and two pain varieties were most prevalent (2.60 ± 0.63). Our findings confirm links between pain, its evolution over time, its multi-modal character, the wide variety of symptoms of PD, and the female sex. Conclusions: Our results demonstrated that the pain felt by PD patients is mainly felt as an electrical current, which contrasts with other studies where the pain is described as burning and itching. Our classification is innovative because it is based on anatomy, whereas those of other authors were based on syndromes.

• Proceedings of the Korean Society of Applied Pharmacology
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• 2007.11a
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• pp.19-27
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• 2007

An important potential of metabolomics-based approach is the possibility to develop fingerprints of diseases or cellular responses to classes of compounds with known common biological effect. Such fingerprints have the potential to allow classification of disease states or compounds, to provide mechanistic information on cellular perturbations and pathways and to identify biomarkers specific for disease severity and drug efficacy. Metabolic profiles of biological fluids contain a vast array of endogenous metabolites. Changes in those profiles resulting from perturbations of the system can be observed using analytical techniques, such as NMR and MS. $^1H$ NMR was used to generate a molecular fingerprint of serum or urinary sample, and then pattern recognition technique was applied to identity molecular signatures associated with the specific diseases or drug efficiency. Several metabolites that differentiate disease samples from the control were thoroughly characterized by NMR spectroscopy. We investigated the metabolic changes in human normal and clinical samples using $^1H$ NMR. Spectral data were applied to targeted profiling and spectral binning method, and then multivariate statistical data analysis (MVDA) was used to examine in detail the modulation of small molecule candidate biomarkers. We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences between healthy and disease population. Such metabolic signatures could provide diagnostic markers for a disease state or biomarkers for drug response phenotypes.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.269-280
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• 2013

In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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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.