• Journal of Korean Medical classics
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• v.21 no.2
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• pp.151-172
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• 2008

This study searched fundamental basis of four-constitution medicine from the principle of "Yeokgyeong(易經)" that is scientific foundations of Dongmu(東武) Ijema(李濟馬)'s four-constitution medicine based on system of "Yeokgyeong(易經)" and looked into the principle of our-constitution medicine ontologically. That is to say, the translation of five phase(五行) that represented in "Hwangjenaegyeong(黃帝內經)" regulated that substance of five phase is spleen-earth(脾土). But four-constitution medicine mentions the substance as heart-earth(心土) in place of spleen-earth. Because of it's standpoint, the differences on meanings between spleen-earth and heart-earth on the substance of five-phase becomes motive to interpret scientific system of four-constitution medicine fundamental wrongly. For that reason, the research of this title is needed. The results was summarized as follows. First, in ontological view point of structure of four-constitution, five phase is substance and phenomenon, in other words it includes earth of unrevealed substance and wood, fire, metal and water of self-manifestation of existence. Second, in axiological view point, the four-constitution represent principles and contents of four virtues of human nature. And so the innate four virtues ontologically based on four-constitution of heaven. Therefore a human being is endowed innately benevolence, courtesy, justice, intelligence of four virtues. Third, the concept of greater and lesser of Eum(陰, yin) and Yang(陽, yang) in Dongmu(東武)'s four-constitution medicine is four-constitution in "Yeokgyeong(易經)". Greater principle(太極) and four-constitution is a relation of substance and phenomenon. Fourth, the origin and structure of four-constitution medicine includes the structure and principle of natural philosophical Eumyang and four-constitution, the human-centric theory and sciences of human nature and natural laws and medical experience of traditional oriental medicine and medical principle.

• Progress in Medical Physics
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• v.22 no.2
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• pp.72-78
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• 2011

The aim of this study is to evaluate the patient's setup errors in TomoTherapy (Hi-Art II, TomoTherapy, USA) Bodyfix system (Medical Intelligence, Ele-kta, Schwabmuchen, Germany) pressure in the vacuum compression, depending on and were evaluated. Bodyfix immobilization system and vacuum pressure was compression applied to the patients who received Tomotherapy thoracic and abdominal area, 21 patients were selected and TomoTehpay treatment total 477 of MVCT images were obtained. The translational (medial-lateral: ML, anterior-posterior: AP, superior-inferior: SI directions) and rolling were recorded and analyzed statistically. Using Pearson's product-moment coefficient and One-way ANOVA, the degree of correlation depending on the different vacuum pressure levels were statistically analyzed for setup errors from five groups (p<0.05). The largest average and standard deviation of systematic errors were 6.00, 5.95 mm in the AP and SI directions, respectively. The largest average of random errors were 4.72 mm in the SI directions. The correlation coefficients were 0.485, 0.244, and 0.637 for the ML-Roll, AP-Vector, and SI-Vector, respectively. SI-Vector direction showed the best relationship. In the results of the different degree of vacuum pressure in five groups (Pressure range: 30~70 mbar), the setup errors between the ML, SI in both directions and Roll p=0.00 (p<0.05) were shown significant differences. The average errors of SI direction in the vacuum pressure of 40 mbar and 70 mbar group were 4.78 mm and -0.74 mm, respectively. In this study, the correlation between the vacuum pressure and the setup-errors were statistically analyzed. The fact that setup-errors in SI direction is dependent in vacuum pressure considerly setup-errors and movement of interal organs was identified. Finally, setup-errors, and it, based on the movement of internal organs in Bodyfix system we should apply more than 50 mbar vacuum pressure. Based on the results of this study, it is suggested that accuracy of the vacuum pressure and the quantitative analysis of movement of internal organs and the tumor should be studied.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.77-82
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• 2004

The purpose of this study is to establish a reference for image acquisition for completion of a standard brain for diverse Korean population, and to develop a management system that saves and manage database of the acquired brain image and personal information of those who were tested. 3D MP-RAGE technique, which has excellent SNR and CNR and reduces the times for image acquisition, was selected for anatomical Image acquisition, and parameter values were obtained for the optimal image acquisition. The database management system was devised to obtain not only anatomical image data but also subjects' basic demographic factors, medical history, handedness inventory state-trait anxiety inventory, A-type personality inventory, self-assessment depression inventory questionnaires of Sasang Constitution Mini-Mental State Examination, intelligence test, and personality test via a survey questionnaire and to save and manage the results of the tests. In addition, this system was designed to have functions of saving, inserting, deleting, searching, and Printing of image da a and personal information of subjects, and to have accessibility to them as well as automatic connection setup with ODBC. This newly developed system may have major contribution to the completion of a standard brain of diverse Korean population in that it can save and manage their image date and personal information.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.1
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• pp.22-27
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• 2021

This study conducted a questionnaire for students of Pusan National University Graduate School of Korean Medicine who practiced using the Oriental Medicine Diagnosis System (ODS). From the questionnaire, this study investigated current state of application and perception of AI in Korean Medicine and explored the direction of ODS improvement and utilization. The survey questions consisted of six questions examining the satisfaction of the diagnostic expert system, five questions evaluating the availability of the diagnostic expert system, and six questions to predict the impact of AI on the Korean medicine community. The survey analysis showed high satisfaction with practice using ODS. On the other hand, the possibility of using ODS, especially in clinical use, was evaluated as relatively low compared to the satisfaction of the practice. Therefore, the overall impact of AI on the Korean medical community is not expected to be large. Although there are difficulties in standardization of clinical data due to the academic characteristics of Korean medicine, it is necessary to continue attempts to apply AI. By actively introducing educational tools using the latest AI techniques to the diagnosis experience and doctor-patient role in a practice, students will be able to increase their satisfaction with their practice and respond appropriately to the state-of-the-art medical environment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.53-60
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• 2004

This paper is the study for the automatic pulse-power diagnostic system to discriminate the four constitutions with the piezo-sensor module and digital signal processing hardware attached on the patient arm-neck and the statistical decision software instead of the fingers and intelligence of a traditional korean doctor. This system can be used as a important medical equipment because this automatically diagnostic system has shown the excellent performance of the 65∼76% correctness against the 50∼66% correctness which the general korean doctors with knowledge and experiences have shown. Additionally, this paper has discussed the excellent characteristics of the automatic discrimination algorithm of the four constitutions.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.27 no.1
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• pp.39-47
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• 2016

Objectives: The aim of this study was to examine the association between current suicidal or violent behavior and deficits of specific neurocognitive variables in child and adolescent inpatient samples diagnosed with mood disorder. Methods: A retrospective review of the charts of mood disorder patients hospitalized at Samsung Medical Center between April 2004 and April 2015 was conducted. Child and adolescent patients aged between 10 and 18 years old and those who finished neurocognitive function testing during their hospitalization were included. Among them patients whose full scale IQ was between 85 and 115 were selected (N=111). Participants were first divided into two age-groups-group Y ($10{\leq}age{\leq}15$, N=54) and group O ($16{\leq}age{\leq}18$, N=57)-because neurocognitive function test tools were different according to age [Wechsler Intelligence Scale for Children (WISC) for 10 to 15-year-old patients, Wechsler Adult Intelligence Scale (WAIS) for 16 to 18-year-old patients]. They were then divided according to their suicidal or violent behavior-non suicidal/violent group (NG), suicidal group (SG), violent group (VG), and both suicidal/violent group (BG). The Child Behavior Checklist (CBCL) was checked for measurement of participants' behavior and the Gordon Diagnostic System was checked for measurement of their attention efficiency. Kruskal-Wallis Test and Tukey test was used to determine the differences in neurocognitive function between groups. Results: O-SG patients showed lower scores on the comprehension subscale of WAIS-III than O-NG patients (${\chi}^2=8.454$, p=.015). O-VG patients showed lower scores on the block design subscales of WAIS than O-SG patients (${\chi}^2=7.496$, p=.024). Y-VG patients showed higher scores in aggressive behavior, externalizing problems, and total problems scores of CBCL. Conclusion: This study showed relationship between specific neurocognitive deficits and suicidal or violent behavior. These relationships were significant in relatively older adolescents.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.65-74
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• 2024

Objective : This study assessed ChatGPT, an artificial intelligence system based on a large language model, for its ability to pass the National Korean Occupational Therapy Licensure Examination (NKOTLE). Methods : Using NKOTLE questions from 2018 to 2022, provided by the Korea Health and Medical Personnel Examination Institute, this study employed English prompts to determine the accuracy of ChatGPT in providing correct answers. Two researchers independently conducted the entire process, and the average accuracy of both researchers was used to determine whether ChatGPT passed over the 5-year period. The degree of agreement between ChatGPT answers of the two researchers was assessed. Results : ChatGPT passed the 2020 examination but failed to pass the other 4 years' examination. Specifically, its accuracy in questions related to medical regulations ranged from 25% to 57%, whereas its accuracy in other questions exceeded 60%. ChatGPT exhibited a strong agreement between researchers, except for medical regulation questions, and this agreement was significantly correlated with accuracy. Conclusion : There are still limitations to the application of ChatGPT to answer questions influenced by language or culture. Future studies should explore its potential as an educational tool for students majoring in occupational therapy through optimized prompts and continuous learning from the data.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.