• Journal of the korean veterinary medical association
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• v.44 no.5
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• pp.396-399
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• 2008

• Journal of the korean veterinary medical association
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• v.50 no.2
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• pp.86-89
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• 2014

• Health Policy and Management
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• v.33 no.4
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• pp.479-488
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• 2023

Background: By applying the suggested criteria for needs-based chronic medical care and long-term care delivery system for the elderly, the current status of delivery system was identified and regional delivery systems were categorized according to quantity and quality of delivery system. Methods: National claims data were used for this study. All claims data of medical and long-term care uses by the elderly and all claims data from long-term care hospitals and nursing homes in 2016 were analyzed to categorize the regional medical and long-term care delivery system. The current status of the delivery system with a high possibility of transition to a needs-based appropriate delivery system was identified. The necessary and actual amount of regional supply was calculated based on their needs, and the structure of delivery systems was evaluated in terms of the needs-based quality of the system. Finally, all regions were categorized into 15 types of medical and care delivery systems for the elderly. Results: Of the total 55 regions, 89.1% of regions had an oversupply of elderly medical and care services compared to the necessary supply based on their needs. However, 69.1% of regions met the criteria for less than two types of needs groups, and 21.8% of regions were identified as regions where the numbers of institutions or regions with a high possibility of transition to an appropriate delivery system were below the average levels for all four needs groups. Conclusion: In order to establish an appropriate community-based integrated elderly care system, it is necessary to analyze the characteristics of the regional delivery system categories and to plan a needs-based delivery system regionally.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.693-704
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• 2024

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

• Radiation Oncology Journal
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• v.17 no.3
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• pp.203-208
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• 1999

Purpose : To evaluate whether spontaneous apoptosis has prognostic value among patients with squamous cell carcinoma of lung. Materials and Methods : Material from 19 patients who received thoracic irradiation between 1990 and 1994 was analyzed. Their stages were II (1), IIIa (8), IIIb (5), and IV (5). Patients were observed from 5 to 67 months (median : 17 months). The spontaneous apoptosis index (AI) and p53 mutation were measured by immunohistochemical stains. Results : AI was found to range from 0 to $1\%$ (median $0.4\%$). Patients with low AI ($AI{\leq}$median) had a much higher distant metastasis rate at diagnosis than patients with high AI. By analysis of prognostic factors for survival, M stage was significant in univariate analysis. AI, chemotherapy, M stage, T stage, and stage were significant in multivariate analysis. The correlation between the AI and p53 mutation was not seen. Conclusion : AI was related with distant metastasis at diagnosis and not with p53 mutation. Also low AI group tended to have shorter survival time than high AI group.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• Journal of the korean veterinary medical association
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• v.47 no.9
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• pp.860-863
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• 2011

최근 양돈협회 자료에 의하면 FMD가 발생하였으나 임상증상이 발현된 돼지만을 부분적으로 살처분한 양돈장이 전국적으로 558호에 약 83만두 규모로 알려져 있다. 또한 앞으로도 환절기나 동절기에 FMD가 재발할 가능성이 있다. 그렇다면 부분 살처분을 실시한 농장은 FMD증상이 어떻게 변화하고 음성화는 가능한 것일까? 기존의 부분 살처분 농장은 FMD바이러스가 농장 내 상존하여 지속적이고 반복적으로 피해를 주지 않을까? FMD가 발생하여 부분 살처분 된 농장이 하루빨리 안정화되고 음성화를 이루기 위해 어떤 조치가 필요한 것인가에 대해 합리적인 대책이 요구된다. 다음의 사례는 AI센터에서 FMD가 발생한 후 음성화 되기 까지 과정을 요약 보고한 자료이다. FMD 음성화에 작은 밑거름이 되었으면 한다.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.557-560
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• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.