• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.9-16
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• 2024

AI or Artificial Intelligence has been a significant tool used in the organisational backgrounds for an effective improvement in the management methods. The processing of the information and the analysis of the data for the further achievement of heightened efficiency can be performed by AI through its data analytics measures. In the medical field, AI has been integrated for an improvement within the management of the medical services and to note a rise in the levels of customer satisfaction. With the benefits of reasoning and problem solving, AI has been able to initiate a range of benefits for both the consumers and the medical personnel. The main benefits which have been noted in the integration of AI would be integrated into the study. The issues which are noted with the integrated AI usage for the medical sector would also be identified in the study. Medical Image Processing has been seen to integrate 3D image datasets with the medical industry, in terms of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). The usage of such medical devices have occurred in the diagnosis of the patients, the development of guidance towards medical intervention and an overall increase in the medical efficiency. The study would focus on such different tools, adhered with AI for increased medical improvement.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05a
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• pp.66-70
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• 2001

The partial discharge testing is widely used in diagnostic measuring technology because it gives low stress to power equipment which is undertaken tests. Therefore it is very useful method compare to previous destructive methods and effective diagnosis method in power system that requires on-line/on-site diagnosis. But partial discharges have very complex characteristics of discharge pattern, so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated system is manufactured and HFPD occurred from those simulator is measured with broad-band antenna in real time, the degradation grade of system is analyzed through produced patterns in simulated target according to the AI/statistics processing.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.44-52
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• 2022

With recent scientific advances and growing interest in AI technologies, AI-based chatbots have been viewed as a practical learning aid for English language development. The purpose of this study is to examine preservice teachers' perceptions on the potential benefits of employing AI chatbots in English instruction and its pedagogical aspects. 28 preservice teachers majoring in English education were asked to use Kuki chatbots for a week with a guidance of a researcher and then report on their perceptions of AI chatbots in terms of perceived usefulness after use, applicability, and educational benefits and drawbacks. Emerging codes and themes were identified and evaluated using Thematic Analysis(TA) based on qualitative data from surveys and interviews. The findings show that six emerging themes were identified, encompassing perspectives on teacher, learner, communication, linguistic, affective, and assessment. The overall findings of this study revealed that AI-based chatbots can play a significant role as learning tools for stimulating interactive communication in a target language. Most preservice primary teachers acknowledge that AI chatbots can be useful as teaching and learning aids for both teachers and students. Furthermore, when applying various learner data to chatbot technology, such as learner assessment and diagnosis, a guided approach is necessary to perform a conversation appropriate for the learner's level and characteristics. Finally, as chatbots have a variety of benefits in terms of affective aspects, they may improve EFL learners' confidence in speaking English and learning motivation.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.83-90
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• 2022

Diabetes is a common disease today, and there are also many cases of developing into serious complications called Diabetic Foot Ulcers(DFU). Diagnosis and prevention of DFU in advance is an important task, and this paper proposes the method. Based on existing studies introduced in the paper, it can be seen that foot pressure and temperature information are deeply correlated with DFU. Introduce the process and architecture of SmarTinsole, an IoT device that measures these indicators. Also, the paper describes the preprocessing process for AI-based diagnosis of DFU. Through the comparison of the measured pressure graph and the actual human step distribution, it presents the results that multiple information collected in real-time from SmarTinsole are more efficient and reliable than the previous study.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.301-307
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• 2010

The purpose of this study was to find out how diagnosis of hyperlipemia differed for according to BMI and %Fat. The included subjects were 224 adult women, they performed physical measurement and BMI measured %Fat by BIA. Blood pressure and lipid profiles were measured in the NPO state. The LDL calculated in using a formula of Friedwald and an atherogenic index was calculated using the serum TC lever divided by th HDL level As a results, HDL decreases so that BMI and %Fat increase and TC, TG, LDL, AI appeared by increasing. There was significant correlation(r=.585) between BMI and %Fat, and lipid profile correlation with BMI is higher than %Fat. In conclusion, diagnosis results of hyperlipemia according to BMI and %Fat could become different conclusively. In study it seems that BMI's diagnosis ability on hyperlipemia is high but the most desirable method uses BMI and %Fat together and evaluates lipid profile.

• The Journal of Korean Medicine
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• v.30 no.3
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• pp.98-105
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• 2009

Objective: Increased aortic and carotid arterial augmentation index (AI) is associated with the risk of cardiovascular disease. The most widely used approach for determining central arterial AI is by calculating the aortic pressure waveform from radial arterial waveforms using a transfer function. But how the change of waveform by applied pressure and the pattern of the change rely on subject's characteristics has not been recognized. In this study, we use a new method for measuring radial waveform and observe the change of waveform and the deviation of radial AI in the same position by applied pressure. Method: Forty-six non-patient volunteers (31 men and 15 women, age range 21-58 years) were enrolled for this study. Informed consent in a form approved by the institutional review board was obtained in all subjects. Blood pressure was measured on the left upper arm using an oscillometric method, radial pressure waves were recorded with the use of an improved automated tonometry device. DMP-3000(DAEYOMEDI Co., Ltd. Ansan, Korea) has robotics mechanism to scan and trace automatically. For each subject, we performed the procedure 5 times for each applied pressure level. We could thus obtain 5 different radial pulse waveforms for the same person's same position at different applied pressures. All these processes were repeated twice for test reproducibility. Result: Aortic AI, peripheral AI and radial AI were higher in women than in men (P<0.01), radial AI strongly correlated with aortic AI, and radial AI was consistently approximately 39% higher than aortic AI. Relationship between representative radial AI of DMP-3000 and peripheral AI of SphygmoCor had strongly correlation. And there were three patterns in change of pulse waveform. Conclusion: In this study, it is revealed the new device was sufficient to measure how radial AI and radial waveform from the same person at the same time change under applied pressure and it had inverse-proportion to applied pressure.

• Clinical Endoscopy
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• v.57 no.3
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• pp.302-308
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• 2024

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

• Progress in Medical Physics
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• v.30 no.2
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.