• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.433-440
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• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Journal of the Korean Society of Radiology
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• v.3 no.3
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• pp.13-21
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• 2009

PET/CT is a machine for imaging in vivo functions or metabolic activities after the administration of radiopharmaceuticals labeled with radioisotope emitting positrons in the body. Recently the number of PET/CT installed in Korean medical institutions is increasing rapidly. In response, the number of PET/CT tests to be used in the diagnosis and treatment of tumors is also increasing every year, and this is increasing the necessity for developing the methods of PET/CT performance evaluation and quality control. Among the test items for the performance evaluation and quality control of PET/CT suggested in NU 2-2007, this study examined spatial resolution test, sensitivity test, image quality, attenuation accuracy & scatter correction test, scatter fraction, count losses and randoms test and accuracy( correction for count losses and randoms).

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.103-109
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• 2016

Corrective control for asynchronous sequential machines is a novel automatic control theory that compensates illegal behavior or adverse effects of faults in the operation of existent asynchronous machines. In this paper, we propose a scheme of diagnosing and tolerating faults occurring to input channels of corrective control systems. The corrective controller can detect faults occurring in the input channel to the controlled machine, whereas those faults happening in the external input channel cannot be detected. The proposed scheme involves an outer operator which, upon receiving the state feedback, diagnoses a fault and sends an appropriate command signal to the controller for tolerating faults in the external input channel.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.426-426
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• 2009

Diagnostic X-ray system is general and basic medical equipment to be used in mostly medical organizations, but being bombed of radioactivity is a big weak point when irradiates a X-ray to the human body so that ICRP restricted the radiation exposure tolerance of the human body. In order to reduce being bombed, the many research and development is now advanced. A lots of diagnostic X-ray machines have currently used due to the increase of occurrence efficiency of X-ray and precisely the output control by using the inverter which is a high speed switching semiconductors. For getting the confidence of the X-ray machine, the same radiation occurrence, same evaluation, and same irradiation condition are necessary when evaluates X-ray irradiation. It is the most important part for the accuracy of the test result and the patient safety. This paper has produced the high voltage occurrence system of full-wave rectification method by using the LC resonance inverter, and evaluated the irradiation reproducibility in order to use it in diagnosis of the patient.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.530-538
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• 2004

A electromagnetic waves are used for sensing in insulation diagnosis at electric machine or equipment. When it a method, waves are transformed into the FFT(Fast Fourier Transform); a kind of the process for data transformation. In a general way, a scientist use frequncy band 30[㎒]～l[㎓] to applied field. If we are measured high frequency band, we will need to a high capacity hardware. Also a antenna has a fault on atmospheric phenomena, outside environment and the like. In this paper We proposed new method for detecting electric equipment faulty state using only electric voltage which is generally measured in the electric and electronic field. It is called the Partition-FFT The analytic method is this first divide measured voltage waves into equal parts, second each deal with give effect to the FFT, finally each results deal with a graphic method and gather graphic. We are compare Partition-FFT with discharge form by tracking tester. As the result it demonstrated that the Partition-FFT is applicable.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.315-319
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• 2001

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide e8rly w, ul1ings in rolling mill. Because dynamics of rolling mill is non-linear. This paper shows a chaotic behaviour of vibration signal in rolling mill using embedding method. Phase plane and Poincare map, FFT and histogram of vibration signal in rolling mill are implemented by qualitative analysis and Fractal dimension, Lyapunov exponent are presented by quantitative analysis.