• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3403-3406
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• 2015

Background: Skin biopsy is the method to assist clinicians to make definite dermatological diagnosis which further helps in holistic management. Skin cancers are relatively rare clinical diagnosis in developing countries like Nepal, but the prevalence is on rise. Objectives: To investigate the profile of skin biopsies and frequencies and pattern of skin cancers in a tertiary care centre of Western Nepal. Materials and Methods: The materials consisted of 434 biopsies (1.37%) out of 31,450 OPD visits performed in the Department of Dermatology, Manipal Teaching Hospital, Pokhara, Nepal, during the period of Dec 2011-Nov 2014. Data were collected and analyzed using SPSS-16 with reference to incidence, age, sex, race and clinical and histopathological features. Results: The commonest disorders observed in biopsies were papulosquamous lesions, skin tuberculosis of different types, benign skin tumors, leprosy, collagen and fungal diseases. Viral diseases were rarely seen, probably due to straight forward clinical diagnosis. Dermatological malignancies accounted for 55/434 (12.67%) of biopsies. Skin disorders in general were commoner in females 280/434 (64%), including malignancies 32/55(58.2%). Mean age of patients with skin cancer was 54.5 years. Facilities for proper laboratory investigation of dermatological disorders will improve the quality of life. Conclusions: The most prevalent lesion in skin biopsies was papulosquamous disorders followed by skin tuberculosis of different types. Dermatological malignancy constituted 55/434 (12.67%) cases. The prevalence of skin malignancy is on rise in Nepalese society probably due to increase in life expectancy and better diagnostic services.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.5
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• pp.47-60
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• 1998

This study was done to identify basic information in classifying nursing diagnoses and nursing interventions needed for the further development of computerized nursing care plans. Data were collected by reviewing charts of 123 home care clients who had active disease, for whom at least one nursing diagnosis was on the chart, and who had been discharged. Data included demographics, medical orders, nursing diagnoses and nursing interventions. The results of the study, which found the most frequent medical diagnoses to be cancer (40.7%) and brain injury (26.8%), showed that 'Impaired Skin Integrity'(18.3%), 'Risk for Infection'(15.0%), 'Altered Nutrition, Less than Body Requirements'(13.8%), and 'Risk for Impaired Skin Integ rity'(9.9%) were the most frequent nursing diagnoses. 'Pressure Ulcer Care'(28.4%) was the most frequent intervention for 'Impaired Skin Integrity', 'Infection Protection'(16.0%) for 'Risk of Infection', 'Nutrition Counseling'(26.8%) for 'Altered Nutrition' and 'Positioning'(22.0%) for 'Risk for Skin Integrity Impairment', Comparison of interventions with the Nursing Intervention Classification(NIC) showed that the most frequent interventions were in the domain 'Basic Physiological' (33.94%), followed by 'Behavioral'(27.8%), and 'Complex Physiological' (22.6%). Interventions related to teaching family to give care at home could not be classified in the NIC scheme. Examination of the frequency of NIC interventions showed that for the domain 'Activity & Exercise Management', 75% of the interventions were used, but for seven domains, none were used. For the domain 'Immobility Management', 93% of the times that an intervention was used, it was 'Positioning', for the domain 'Tissue Perfusion Management', 'IV Therapy' (59.1%) and for the domain 'Elimination Management', 'Tube Care: Urinary'(54.0%). The nursing diagnoses 'Altered Urinary Elimination' and 'Im paired Physical Mobility' were both used with these clients, but neither 'Fluid Volume Deficit' nor 'Risk of Fluid Volume Deficit' were used rather 'IV Therapy' was an intervention for 'Altered Nutrition, Less than Body Requirements', A comparison of clients with cancer and those with brain injury showed that interventions for the nursing diagnosis 'Impaired Skin Integrity' were more frequent for the clients with cancer, interventions for 'Risk of Infection' were similar for the two groups but for clients with cancer there were more interventions for' Altered Nutrition'. Examination of the nursing diagnoses leading to the intervention 'Positioning' showed that for both groups, it was either 'Impaired Skin Integrity' or 'Risk for Skin Integrity Impairment'. This study identified a need for further refinement in the classification of nursing interventions to include those unique to home care and that for the purposes of computerization identification of the nursing activities to be included in each intervention needs to be done.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.1
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• pp.5-8
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• 2010

Opened a court in February 10, 2006, a rule of safety management of the diagnosis radiation system was promulgated for safety of the radiation worker, patients and patients' family members. The purpose of this rule is to minimize the risk of being exposed to radiation during the process of handling X-ray. For this reason, we manufactured shielding device of mobile X-ray unit collimator for diminution of skin dose. Shielding device is made to a thickness of Pb 0.375mm. For portable chest radiography, we measured skin dose 50cm from center ray to 200cm at intervals of 20cm by Unfors Xi detector. As a result, a rule of safety management of the diagnosis radiation system has been strengthened. But there are exceptions, such as ER, OR, ICU to this rule. So shielding device could contribute to protect unnecessary radiation exposure and improve nation's health.

• Biomolecules & Therapeutics
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• v.32 no.1
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• pp.1-12
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• 2024

Adverse drug reactions (ADRs) are an inherent aspect of drug use. While approximately 80% of ADRs are predictable, immune system-mediated ADRs, often unpredictable, are a noteworthy subset. Skin-related ADRs, in particular, are frequently unpredictable. However, the wide spectrum of skin manifestations poses a formidable diagnostic challenge. Comprehending the pathomechanisms underlying ADRs is essential for accurate diagnosis and effective management. The skin, being an active immune organ, plays a pivotal role in ADRs, although the precise cutaneous immunological mechanisms remain elusive. Fortunately, clinical manifestations of skin-related ADRs, irrespective of their severity, are frequently rooted in immunological processes. A comprehensive grasp of ADR morphology can aid in diagnosis. With the continuous development of new pharmaceuticals, it is noteworthy that certain drugs including immune checkpoint inhibitors have gained notoriety for their association with ADRs. This paper offers an overview of immunological mechanisms involved in cutaneous ADRs with a focus on clinical features and frequently implicated drugs.

• Annals of Clinical Neurophysiology
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• v.14 no.1
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• pp.1-6
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• 2012

Low back pain is a common clinical condition with heterogeneous causes and challenges to manage. High prevalence and numerous assessments result in an enormous socioeconomic burden. Clinician must conduct efficient and stepwise evaluation process to rule out serious spinal pathology, neurologic involvement, and identify risk factors for chronicity. The process can be achieved through the focused history taking and physical examination. Certain factors related to serious spinal pathology include age (>50 years), trauma, unexplained fever, recent urinary or skin infection, unrelenting night or rest pain, unexplained weight loss, osteoporosis, immunosuppression, steroid use, and widespread neurological symptoms. In non-specific low back pain, diagnostic imaging and laboratory studies are often unnecessary and can disturb an appropriate management. For the management of acute low back pain, patient education and medication such as acetaminophen, non-steroidal anti-inflammatory drugs, and muscle relaxants are recommended. For chronic low back pain, behavior therapy, back exercise, and spinal manipulation are beneficial. The evidence based approach could improve success rate of management, result in prevention of acute low back pain from being chronic intractable pain.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Archives of Plastic Surgery
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• v.41 no.5
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• pp.500-504
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• 2014

Background Skin cancer is the most prevalent cancer by organ type and referral accuracy is vital for diagnosis and management. The British Association of Dermatologists (BAD) and literature highlight the importance of accurate skin lesion examination, diagnosis and educationally-relevant studies. Methods We undertook a review of the relevant literature, a national audit of skin lesion description standards and a study of speciality training influences on these descriptions. Questionnaires (n=200), with pictures of a circular and an oval lesion, were distributed to UK dermatology/plastic surgery consultants and speciality trainees (ST), general practitioners (GP), and medical students (MS). The following variables were analysed against a pre-defined 95% inclusion accuracy standard: site, shape, size, skin/colour, and presence of associated scars. Results There were 250 lesion descriptions provided by 125 consultants, STs, GPs, and MSs. Inclusion accuracy was greatest for consultants over STs (80% vs. 68%; P<0.001), GPs (57%) and MSs (46%) (P<0.0001), for STs over GPs (P<0.010) and MSs (P<0.0001) and for GPs over MSs (P<0.010), all falling below audit standard. Size description accuracy sub-analysis according to circular/oval dimensions was as follows: consultants (94%), GPs (80%), STs (73%), MSs (37%), with the most common error implying a quadrilateral shape (66%). Addressing BAD guidelines and published requirements for more empirical performance data to improve teaching methods, we performed a national audit and studied skin lesion descriptions. To improve diagnostic and referral accuracy for patients, healthcare professionals must strive towards accuracy (a circle is not a square). Conclusions We provide supportive evidence that increased speciality training improves this process and propose that greater focus is placed on such training early on during medical training, and maintained throughout clinical practice.