• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.55-61
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• 2020

The various types of radiation-generator devices have been used in animal hospitals, and the safety for radiation workers is becoming important in Korea. This study investigated and analyzed the radiation safety management for diagnostic radiation-generator devices and radiation workers of animal hospital. The number of radiation-generator devices and radiation workers of animal hospital increased from 2,138 to 2,972 and from 2,644 and 5,733 for six years. The number of general X-ray, CT, C-arm, portable and dental X-ray in 2019 were 2,204, 58, 67, 770, and 14. The number of veterinarian, veterinary nurse, veterinary assistant, and others in 2019 were 4,236, 1,080, 404, and 13. The average exposure dose of radiation workers in 2018 were 0.21mSv in surface dose, 0.18mSv in depth doses. This study is expected to be the basic data for the safety management of radiation-generating devices and radiation workers in animal hospital.

• The Journal of Korean Obstetrics and Gynecology
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• v.28 no.3
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• pp.1-10
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• 2015

Objectives Diagnosis System of Oriental Medicine (DSOM) was made as a computerized assistant program for oriental medicine doctors to be able to diagnose with statistical basis. Then DSOM uses questionnaires filled out by respondents with explanatory guide. But if the respondents misunderstand the meaning of the passages, the results were quite the opposite. Methods This study was designed to investigate the diagnostic correspondent rates between DSOM and TKM practitioners. First, let the respondents answer to DSOM. After that, three doctors diagnosed the respondents and marked 'p' when they diagnose that the respondent had the pathogenic factors, marked 'n' when they diagnose that the respondent had the pathogenic factors but not severs, and did not marked when they diagnose that the respondent didn't have the pathogenic factors. Finally, this study was investigated the correspondent rates of diagnosis between DSOM and doctors. Results In the pathogenic factor of three including insufficiency of Yin (陰虛), the correspondent rates were 90%. In the pathogenic factor of nine including deficiency of qi (氣虛), the correspondent rates were 80%. In the pathogenic factor of four including blood stasis (血瘀), the correspondent rates were 70%. In HH and HL, they showed the correspondent rates of 61.77%. The correspondent rate of heat (熱) was highest (96.88%). The correspondent rate of insufficiency of Yang (陽虛) was lowest (0%). In LH and LL, they showed the correspondent rates of 88.31%. The correspondent rate of blood stasis (血瘀) was lowest (71.76%). They all showed the correspondent rates of over 70%. Conclusions In DSOM and Doctors' diagnose, they showed the correspondent rates of 83.60%.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.379-388
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• 2022

Objectives: Throughout history, societies have been impacted by inequality. Many studies have been conducted on the topic more broadly, but only a few have investigated inequalities in out-of-pocket health payments (OHP). This study measures OHP inequality trends among the Iranian households. Methods: This study used data from the Iranian Statistics Center on Iranian household income and expenditures. The analysis included a total of 995 300 households during the 36 years from 1984 to 2019. The Gini coefficient, Atkinson index, and Theil index were calculated for Iranian OHP. Results: Average Iranian household OHP increased from 33 US dollar (USD) in 1984 to 47 USD in 2019. During this 36-year span, the average±standard deviation Gini coefficient for OHP was 0.73±0.04, and the Atkinson and Theil indexes were 0.68±0.05 and 1.14±0.29, respectively. The Gini coefficients for the subcategories of OHP of outpatient diagnostic services, medical assistant accessories, hospital inpatient services, and addiction cessation were 0.70, 0.61, 0.84, and 0.64, respectively. Conclusions: In this study, we scrutinized trends of inequality in the OHP of Iranian households. Inequality in OHP decreased slightly over the past four decades. An analysis of trends among different subgroups revealed that affluent households, such as households with insurance coverage and households in higher income deciles, experienced higher inequality. Therefore, lower inequality in health care expenditures may be related to restricted access to health care services in Iran.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.595-605
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• 2012

Various methods for measuring strabismus have been developed and used in clinical diagnosis. However, most of them are based on the visual inspection by clinicians. For this reason, there is a high possibility of subjective evaluation in clinical decisions and they are only useful for cooperative patients. Therefore, the development of a more objective and reproducible method for measuring strabismus is needed. In this paper, we introduce a new software to complement the limitations of previous diagnostic methods. Firstly, we simply obtained facial images of patients and performed several preprocessing steps based on the spherical RGB color model with them. Then, the measurement of strabismus was performed automatically by using our 3D eye model and mathematical algorithm. To evaluate the validity of our software, we performed statistical correlation analysis of the results of the proposed method and the Krimsky test by two clinicians for ten patients. The coefficients of correlation for two clinicians were very high, 0.955 and 0.969, respectively. The coefficient of correlation between two clinicians also showed 0.968. We found a statistically significant correlation between two methods from our results. The newly developed software showed a possibility that it can be used as an alternative or effective assistant tool of previous diagnostic methods for strabismus.

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.6
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• pp.1359-1367
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• 2008

DSOM(Diagnosis System of Oriental Medicine) was made as a computerized assistant program for oriental medicine doctors to be able to diagnose with statistical basis. Then DSOM uses questionnaires filled out by subjects without enough explanatory guide. If the subject misunderstand the meaning of the passages, we might not rely on that result. So I designed this study to investigate the diagnostic correspondent rates between DSOM and practitioners. First, let the respondents answer to DSOM(DSOM-Ⅰ for the rest). After that, three doctors diagnosed the respondents and marked how much they had symptoms about 16 pathogenic factors in the score range 0${\sim}$5('0' means they didn't have that symptom, '1' means they had that symptom but mild, '3' means they had that symptom moderately, '5' means they had that symptom severely. And let the respondents answer to DSOM(DSOM-Ⅱ for the rest) again. Finally, we investigated the correspondent rates of diagnosis between DSOM-Ⅰ,Ⅱ and doctors'. We obtained conclusions as following. In the comparison of output frequency rate of the pathogenic factors, the difference between DSOM-Ⅰ and Ⅱ was 1%. In the correspondent rates of diagnosis between DSOM-Ⅰ,Ⅱ and doctors', In DSOM-Ⅰ and Ⅱ answered by subjects two times respectively, the correspondent rate was highest in insufficiency of Yang(陽虛) and liver(肝) as 93.2%, lowest in damp(濕) as 69.5% and showed 81.9% in all 16 pathogenic factors mean. In DSOM-Ⅰ and Ⅱ, and Doctors' diagnose, they showed the complete correspondent rates of 15.3${\sim}$61.0%, 15.3${\sim}$59.3% in individual pathogenic factor, 36.5%, 37.3% in all 16 pathogenic factors mean each, and within ${\pm}$1 errorrange, they showed the correspondent rates of 32.2${\sim}$93.2%, 35.6${\sim}$89.8% in individual pathogenic factor, 67.6%, 67.3% in all 16 pathogenic factors mean each, and within ${\pm}$2 error range, they showed the correspondent rates of 62.7${\sim}$98.3%, 71.2${\sim}$100% in individual pathogenic factor, 85.1 87.6%% in all 16 pathogenic factors mean each. In the correspondent rates of the severe case, In the cases that the Doctors' diagnostic score mean was over 3(the severity of disease is middle), there were deficiency of qi(氣虛), stagnation of qi(氣滯), blood stasis(血瘀), damp(濕), liver(肝), heart(心), spleen(脾) and they all showed the correspondent rates of over 60 except blood stasis(血瘀). In the cases that the weighed pathogenic factor was above 9, the correspondent rates were 50${\sim}$100%. deficiency of qi(氣虛), blood-deficiency(血虛), stagnation of qi(氣滯), blood stasis(血瘀), insufficiency of Yin(陽虛), insufficiency of Yang(陽虛), coldness(寒), heat (熱), damp(濕), dryness(燥), liver(肝), heart(心), spleen(脾), kidney(腎), phlegm(痰).

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.

• Journal of Korean Medical classics
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• v.23 no.4
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• pp.103-153
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• 2010

"Sanghanjeonsaengjip(傷寒全生集)" is a classic medical work, written by the outstanding physician Dohwa(陶華) in the Ming Dynasty. The characteristic of "SangHanJeonSaengJip" is that this book succeeded to the spirit of pattern identification and treatment of Treatise on Cold Damage Diseases, newly changed a table of contents by symptoms, and together with this indicated the prescriptions in accordance with diswase-pattern at "YujeunghwalInseo(類證活人書)", "Hwajegookbang(和劑局方)" etc. Also because this kept the existing ephedra decoction, cinnamom twig decoction, minor decoction of bupleurum, decoction for reinforcing middle-energizer and replenishing qi etc.'s name on and unlikely indicated the medicine composition, it caused confusion, but at the later ages "Euhakipmun(醫學入門)" the so-called 'Doci(陶氏)' was added to the prescription name, so we are able to distinguish. Together with this, this book dose not indicate the dosage of medicine and indicates the first, the second, and the third classes[上中下] below medicine. As this dose not mean the three grades of quality"good, fair, and poor[上中下] of "Shennong's Classic of Materia Medica" but expresses the sovereign medicinal as the first class[上], minister medicinal as the second class[中] and assistant and courier medicinal as the third class[下], doctors can voluntarily decide the dosage of medicine in accordance with the degree of disease. At this thesis, I single out ten chapters in contents of 2nd volume named Hyeong(亨) corresponding to the details, among "Sanghanjeonsaengjip(傷寒全生集)". I discussed superficial fever types of exogenous febrile disease in chapter 1, aversion to cold types of exogenous febrile disease in chapter 2, syndrome caused un-sufficient sweating in chapter 3, organic fever types of exogenous febrile disease in chapter 4, aversion to wind types of exogenous febrile disease in chapter 5, Tidal fever types of exogenous febrile disease in chapter 6, Alternative attacts of chills and fever in chapter 7, Dysphoria with smothery sensation in chapter 8, Fidgetiness of exogenous febrile disease in chapter 9, and Headache of exogenous febrile disease in chapter 10, and together with this I discussed, in detail, which influence the prescriptions which are listed on each chapter have caused on future generations In accordance with this, I think that the above-mentioned symptoms and prescriptions are important when I research cold damage and warm disease study. So I orderly research revision, annotation, rendering and an investigation.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.251-256
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• 2019

A capsule endoscopy is a medical device that can capture an entire digestive organ from the esophagus to the anus at one time. It produces a vast amount of images consisted of about 8~12 hours in length and more than 50,000 frames on a single examination. However, since the analysis of endoscopic images is performed manually by a medical imaging specialist, the automation requirements of the analysis are increasing to assist diagnosis of the disease in the image. Among them, this study focused on automatic detection of polyp images. A polyp is a protruding lesion that can be found in the gastrointestinal tract. In this paper, we propose a weighted-image generation method to enhance the polyp image learning by multi-scale analysis. It is a way to extract the suspicious region of the polyp through the multi-scale analysis and combine it with the original image to generate a weighted image, that can enhance the polyp image learning. We experimented with SVM and RF which is one of the machine learning methods for 452 pieces of collected data. The F1-score of detecting the polyp with only original images was 89.3%, but when combined with the weighted images generated by the proposed method, the F1-score was improved to about 93.1%.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.661-672
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• 2022

After the outbreak of the SARS-CoV2 virus that causes COVID-19, it spreads around the world with the number of infections and deaths rising rapidly caused a shortage of medical resources. As a way to solve this problem, chest X-ray diagnosis using Artificial Intelligence(AI) received attention as a primary diagnostic method. The purpose of this study is to comprehensively analyze the detection of COVID-19 via AI. To achieve this purpose, 292 studies were collected through a series of Classification methods. Based on these data, performance measurement information including Accuracy, Precision, Area Under Cover(AUC), Sensitivity, Specificity, F1-score, Recall, K-fold, Architecture and Class were analyzed. As a result, the average Accuracy, Precision, AUC, Sensitivity and Specificity were achieved as 95.2%, 94.81%, 94.01%, 93.5%, and 93.92%, respectively. Although the performance measurement information on a year-on-year basis gradually increased, furthermore, we conducted a study on the rate of change according to the number of Class and image data, the ratio of use of Architecture and about the K-fold. Currently, diagnosis of COVID-19 using AI has several problems to be used independently, however, it is expected that it will be sufficient to be used as a doctor's assistant.