• Parasites, Hosts and Diseases
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• v.59 no.6
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• pp.615-623
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• 2021

Human sparganosis is a food-borne parasitic disease caused by the plerocercoids of Spirometra species. Clinical diagnosis of sparganosis is crucial for effective treatment, thus it is important to identify sensitive and specific antigens of plerocercoids. The aim of the current study was to identify and characterize the immunogenic proteins of Spirometra erinaceieuropaei plerocercoids that were recognized by patient sera. Crude soluble extract of the plerocercoids were separated using 2-dimensional gel electrophoresis coupled with immunoblot and mass spectrometry analysis. Based on immunoblotting patterns and mass spectrometry results, 8 antigenic proteins were identified from the plerocercoid. Among the proteins, cysteine protease protein might be developed as an antigen for diagnosis of sparganosis.

• Journal of Gastric Cancer
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• v.24 no.3
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• pp.327-340
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• 2024

Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

• Journal of Oral Medicine and Pain
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• v.23 no.4
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• pp.419-432
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• 1998

The purpose of this Study was to examine the efficacy and compliance of a mandibular advancement device(MAD) according to the severity of sleep apnea in the snorers and obstructive sleep apnea patients. Fifty-four patients (45 males, 9 females, aged 20 - 68years ) who visited Seoul National Uiversity Dental Hospital(SNUDH) to seek for the treatment of snoring and sleep apnea were classified into four groups according to the results of the nocturnal polysomnography and they were instructed to wear MAD regularly which was designed to increase the size of the upper airway by advancing the mandible. The evaluation of the efficacy and compliance of the MAD according to the severity of apnea and the duration after the usage of MAD ( 1week, 1month, 3months, 6months, 12months) was made by using quesionnaires mad in Department of Oral Medicine and Oral diagnosis, SNUDH. The obtained results were as follows : 1. All subjects results were habitual snoreres and 43 patients(79.6%) complained the loudness of snoring that can be heard out of the room. 2. Apnea index(AI) of the total subjects was mean 29.4$\pm$26.9 and respiratory disturbance index(RDI)was mean 37.6$\pm$28.0. And there was nodifference in the efficacy and the compliances of MAD according to the severity of apnea. 3. The severityi of apnea by the questionnaires significantly corresponded with the results of nocturnal polysomnography, and this fact potentiated the diagnostic value of the questionnaire. 4. after the usage of MAD, there was significant improvement in the frequency of snoring, the loudness of snoring, frequency of apnea, daytime sleepiness nad the refreshment after sleep(p<0.001) regardless of the apnea index(AI) and respiratory distrubance index(RDI). 5. The degree of the satisfaction with MAD was mean 74.4$\pm$18.4% and that of the discomfort with the MAD was 31.4$\pm$19.6%. But there was no serious complication in occlusion and temporomandibular joint with the usage of MAD and the duration of the discomfort was mean 3.3$\pm$2.2 weeks. 6. Forty-one patients(75.9%) continued the usage of MAD but thirteen patients(24.1%) stopped the use of MAD because of the discomforts and insufficient results with it.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.53-59
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• 2021

In this paper, we propose a BERGPT-chatbot, a domestic AI chatbot that can alleviate negative emotions based on text input such as 'Replika'. We made BERGPT-chatbot into a chatbot capable of mitigating negative emotions by pipelined two models, KR-BERT and KoGPT2-chatbot. We applied a creative method of giving emotions to unrefined everyday datasets through KR-BERT, and learning additional datasets through KoGPT2-chatbot. The development background of BERGPT-chatbot is as follows. Currently, the number of people with depression is increasing all over the world. This phenomenon is emerging as a more serious problem due to COVID-19, which causes people to increase long-term indoor living or limit interpersonal relationships. Overseas artificial intelligence chatbots aimed at relieving negative emotions or taking care of mental health care, have increased in use due to the pandemic. In Korea, Psychological diagnosis chatbots similar to those of overseas cases are being operated. However, as the domestic chatbot is a system that outputs a button-based answer rather than a text input-based answer, when compared to overseas chatbots, domestic chatbots remain at a low level of diagnosing human psychology. Therefore, we proposed a chatbot that helps mitigating negative emotions through BERGPT-chatbot. Finally, we compared BERGPT-chatbot and KoGPT2-chatbot through 'Perplexity', an internal evaluation metric for evaluating language models, and showed the superity of BERGPT-chatbot.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1471-1475
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• 2014

Background: The epidemiological patterns of cancer incidence have been investigated widely in western countries. Nevertheless, information is quite limited in Jiangxi province, southern China. Materials and Methods: All data were reported by 6 population-based cancer registries in Jiangxi Province. The results were presented as incidence rates of cases by site (ICD-10), sex, crude rate (CR), age-standardized rates (ASRs) and truncated incidence rate (TR) per 100,000 person-years, using the direct method of standardization to the world population. Results: 8,765 new cancer cases were registered in our study during the period 2009-2011. Diagnosis of cancer was based on histopathology in 61.0%, clinical or radiology findings in 4.87% and death certificate only (DCO) in 3.0% of the cases. The median age at diagnosis was 62.0 years (mean, 61; standard deviation, 15). The ASRs were 170.8 per 100,000 for men and 111.2 for women. The ASRs for all invasive cancers from the urban areas (145.7 per 100,000) was higher than that of rural areas (137.1). Incidence rates for lung cancer were higher in rural (35.8) than in urban areas (27.0). Similarly, relatively high rates were observed for stomach cancer in rural (20.1) relative to urban areas (15.5). Conclusions: Our results reveal that the most common cancers were breast and lung in women and lung and liver in men. Interestingly, this study suggested a higher incidence rates for lung and stomach cancer in rural males than in urban population, which may suggest other potential causes, such as over-consumption of smoked meats and high prevalence of Helicobacter pylori infection, respectively. Public education and the promotion of healthy lifestyles should be actively carried out.

• Korean Journal of Poultry Science
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• v.46 no.4
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• pp.235-247
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• 2019

Serological monitoring has been conducted worldwide for early diagnosis of disease and monitoring of immune status in poultry. This study was conducted to evaluate the immune status of layers with sera submitted to the Avian Disease Laboratory, Chungbuk National University from 2015 to 2017. The test results were analyzed by the time submitted and by the age of the chicks. Low pathogenic avian influenza (LPAI) showed a low positive rate of antibody compared with those of Newcastle disease, indicating that domestic vaccination against LPAI was not sufficient. The antibody profile of infectious bronchitis (IB) depicted high level of titer and a low tendency of CV as compared to the uninfected control flocks, which indicated that most layer farms have been exposed to the field IB virus. In case of avian metapneumovirus infection (aMPV) and Mycoplasma synoviae (MS), since the introduction of the vaccine in 2011 and 2017, respectively, the positive rate and the titer level were higher than those in pevious times. No significant difference in the changes of seasonal result was observed, indicating proper vaccination and improvement in biosecurity and management.

• Advances in pediatric surgery
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• v.9 no.1
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• pp.19-23
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• 2003

Necrotizing Enterocolitis (NEC) is usually a disease of premature infants, but occasionally it affects the term neonate. Twenty-five infants with NEC were treated at Asan Medical Center between January 2000 and December 2002, and 13 of them were term infants. In each case, the diagnosis of NEC was established by a clinical illness fulfilling the Bell's stage II or III NEC as modified by Walsh or by surgical findings. There were six males and seven females. The birth weight was from 1,960 to 3,700 g. The age at diagnosis was from 1 to 40 days. Four patients had congenital heart disease: one of who had hypothyroidism and cleft palate. Abdominal distension was present in all, and bloody stools in four. One patient had history of hypoglycemia, three had Rota viral infection. Eight patients had leucopoenia (<$5.0{\times}10^9/L$), seven had thrombocytopenia (<$100{\times}10^9/L$), and three severe thrombocytopenia (<$50{\times}10^9/L$). Laparotomy was required in 10 of the 13 patients. Indications for operation in the acute phase were failure to respond to aggressive medical therapy in five, and perforation in three patients. There were two late phase operations for intestinal stricture and fistula. There were no operative complications. Ten of thirteen patients survived (76.9%). Two patients died of septic complication. There was a delayed death due to heart failure. There was a significant difference in survival according to platelet count ($50{\times}10^9/L$) (p<0.05). Congenital heart disease and Rota viral infection are associated with NEC in term infants and thrombocytopenia and leucopoenia may be surgical indications.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.