• Korean Journal of Digital Imaging in Medicine
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• v.16 no.1
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• pp.35-42
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• 2014

Heart related special images are classified as Cardiac US, XA, CT, MRI. Several Problem is caused by image compression, control and medical support point, so most big hospitals have created a Cadiac PACS differentially in past years. For this reason, create a conflict in inner colleague and patient, protector that result from 2 data processing server operating independently in 1 medical center area. For this reason, we sugges an alternative model of best medical control process together with understand the current situation on medical facility.

• 대한생식의학회:학술대회논문집
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• 2003.12a
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• pp.157-157
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• 2003

• Maxillofacial Plastic and Reconstructive Surgery
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• v.29 no.2
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• pp.113-122
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• 2007

Purpose: Platelet-rich plasma (PRP) is known to accelerate and/or enhance hard and soft tissue healing and regeneration. As such, PRP has been used in various clinical fields of surgery. Recently there have been several attempts to use PRP in the field of tissue engineering. However, some controversies still exist on exact mechanism and benefits of PRP. Therefore various animal experiments are necessary to reveal the effect of the PRP. However, even if animal experiment is performed, the efficacy of the experiment could not be validated due to absence of an animal PRP model. The purpose of this study is to establish rat PRP model by comparing several PRP fabricating methods, and to assay growth factor concentration in the PRP. Materials and methods: Rat blood samples were collected from nine SD rat (body weight: 600-800g). PRP was prepared using three different PRP fabricating methods according to previously reported literatures. (Method 1: 800 rpm, 15 minute, single centrifuge; Method 2: 1000 rpm, 10 minute, double centrifuge; Method 3: 3000 rpm, 4min and 2500 rpm, 8 min, double centrifuge). Platelet counts were evaluated in an automated machine before and after PRP fabrications. In terms of growth factor assay, prepared PRP were activated with 100 unit thrombin and 10% calcium chloride. Growth factor (PDGF-BB, VEGF) concentrations on incubation time were determined by sandwich-ELISA technique. Results: An average of 3ml (via infraorbital venous plexus) to 15ml (via celiac axis) the rat blood could be collected. By using Method 3 (3000 rpm, 4 min and 2500 rpm, 8 min, double centrifugation), around 1.5ml of PRP could be prepared. This method allowed us to concentrate platelet 3.77-fold on average. PDGF-BB concentration (mean, 1942.10 pg/ml after 1 hour incubation) and VEGF concentration (mean, 952.71 pg/ml after 1 hour incubation) in activated PRP were higher than those in untreated blood. Also PDGF-BB showed constant concentration during 4-hour incubation, while VEGF concentration was decreased after 1 hour. Conclusion: Total 11,000 g minute separation and condensation double centrifuge method can produce efficient platelet-rich plasma. Platelet-rich plasma activated with thrombin has showed higher concentrations of growth factors such as PDGF-BB and VEGF, compared to the control group. Platelet-rich plasma model in a rat model was confirmed in this study.

• 대한방사선방어학회:학술대회논문집
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• 2009.04a
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• pp.86-87
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• 2009

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• Journal of Information Processing Systems
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• v.12 no.4
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• pp.711-723
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• 2016

With the rapid development of both ubiquitous computing and the mobile internet, big data technology is gradually penetrating into various applications, such as smart traffic, smart city, and smart medical. In particular, smart medical, which is one core part of a smart city, is changing the medical structure. Specifically, it is improving treatment planning for various diseases. Since multiple treatment plans generated from smart medical have their own unique treatment costs, pollution effects, side-effects for patients, and so on, determining a sustainable strategy for treatment planning is becoming very critical in smart medical. From the sustainable point of view, this paper first presents a three-dimensional evaluation model for representing the raw medical data and then proposes a sustainable strategy for treatment planning based on the representation model. Finally, a case study on treatment planning for the group of "computer autism" patients is then presented for demonstrating the feasibility and usability of the proposed strategy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1821-1825
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• 2015

Background: Associations between the NQO1 C609T polymorphism and hepatocellular carcinoma (HCC) risk are a subject of debate. We therefore performed the present meta-analysis to evaluate links with HCC susceptibility. Materials and Methods: Several major databases (PubMed, EBSCO), the Chinese national knowledge infrastructure (CNKI) and the Wanfang database were searched for eligible studies. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to measure the strength of associations. Results: A total of 4 studies including 1,325 patients and 1,367 controls were identified. There was a significant association between NQO1 C609T polymorphism and HCC for all genetic models (allelic model: OR=1.45, 95%CI=1.23-1.72, p<0.01; additive model: OR=1.96, 95%CI=1.57-2.43, p<0.01; dominant model: OR=1.62, 95%CI=1.38-1.91, p<0.01; and recessive model: OR=1.53, 95%CI=1.26-1.84, p<0.01). On subgroup analysis, similarly results were identified in Asians. For Asians, the combined ORs and 95% CIs were (allelic model: OR=1.50, 95%CI=1.24-1.82, p<0.01; additive model: OR=2.11, 95%CI=1.48-3.01, p<0.01; dominant model: OR=1.69, 95%CI=1.42-2.02, p<0.01; and recessive model: OR=1.59, 95%CI=1.16-2.19, p<0.01). Conclusions: The current meta-analysis suggested that the NQO1 C609T polymorphism could be a risk factor for developing HCC, particularly in the Chinese population.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3153-3157
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• 2015

Background: Incidence and mortality rates for cancer have increased dramatically in the recent 30 years in Taiwan. However, not all patients receive treatment. Treatment refusal might impair patient survival and life quality. In order to improve this situation, we proposed this study to evaluate factors that are related to refusal of treatment in cancer patients via a cancer case manager system. Materials and Methods: This study analysed data from a case management system during the period from 2010 to 2012 at a medical center in Northern Taiwan. We enrolled a total of 14,974 patients who were diagnosed with cancer. Using the PRECEDE Model as a framework, we conducted logistic regression analysis to identify independent variables that are significantly associated with refusal of therapy in cancer patients. A multivariate logistic regression model was also applied to estimate adjusted the odds ratios (ORs) with 95% confidence intervals (95%CI). Results: A total of 253 patients (1.69%) refused treatment. The multivariate logistic regression result showed that the high risk factors for refusal of treatment in cancer patient included: concerns about adverse effects (p<0.001), poor performance(p<0.001), changes in medical condition (p<0.001), timing of case manager contact (p=.026), the methods by which case manager contact patients (p<0.001) and the frequency that case managers contact patients (${\geq}10times$) (p=0.016). Conclusions: Cancer patients who refuse treatment have poor survival. The present study provides evidence of factors that are related to refusal of therapy and might be helpful for further application and improvement of cancer care.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.489-497
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• 2001

This paper describe design and implementation of a medical multi-agent system platform called MAMI (Multi-Agent system for Medical Image), which provides intelligent medical information services. The most important component of MAMI is a medical multi-agent system platform that supports a physical environment that medical agents can be deployed. MAMI follows FIPA (Foundation for Intelligent Physical Agent)\`s agent management reference model. In MAMI, COM(Common Object Model) and XML (eXtensibel Markup Language) for encoding ACL (Agent Communication Language) are used for multi-agent communications. In MAMI, a medical staff is conceptualized as an agent and integrated with multi-agent systems. MAMI agent platform provides an infrastructure applicable to share necessary knowledge between human agents and software agents. So MAMI makes intelligent medical information services easier.