• Title/Summary/Keyword: Gastroscope

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Recent Advances in Diagnosis of Gastrointestinal Disease (소화기질환 진단의 최신지견)

  • Choi, Ho-Seung;Kim, Jae-Kwan;Choi, Seo-Hyung
    • The Journal of the Society of Korean Medicine Diagnostics
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    • v.13 no.1
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    • pp.1-9
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    • 2009
  • Objectives : If patients notice a symptom indicating inveterate dyspepsia but they don't have any problem around gastroscope, they get diagnosed as a functional dyspepsia or an imaginary stomach disease, but to overcome the limitations of these diagnoses, we are analyzing them for the common feature and are looking for a new diagnostics for them. Methods : Based on our survey with 122 patients with inveterate dyspepsia, we analyzed the period of onset, eating habits, the main symptoms, and observations on the gastroscope. We also analyzed the function of the stomach and intestines by EAV examinations, and the outer walls of the stomach and intestines by subdividing the level of coagulation into six with abdominal palpation. Results : We figured out that people who appeals about inveterate dyspepsia have had long period of onset, and that they had bad eating habits, shoulder stiffness, neck stiffness, headache, dizziness, etc. These are all the similar symptoms beside dyspepsia, which indicates that it is a syndrome. From about 70%, they didn't had particular problem in gastroscope, and as to be seen from the result of EAV examination, their stomachs and intestines had become functionless. Also, we were able to feel a stiffened tissue through abdominal palpation. Conclusion : Through this investigation, we found out that what the gastroscope can not find so that gets diagnosed as a functional dyspepsia or an imaginary stomach disease can be diagnosed as a syndrome called damjeok by overcoming the limitation utilizing the survey, EAV examinations, and abdominal palpation. We can find a mighty significance from the fact that it can be diagnosed as a syndrome.

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A Rare Case of Airway Foreign Body Removed by Gastroscope (위내시경으로 제거한 희귀한 기도이물 1예)

  • 김정주;왕준호;이재동
    • Korean Journal of Bronchoesophagology
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    • v.8 no.1
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    • pp.96-99
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    • 2002
  • Recently we have experienced an about 15cm long foreign body lodged at trachea in the adult patient, it was a toothbrush bar of which the head had been removed by patient himself. Its tip was shown above vocal cord on videogastroscope, so we removed the toothbrush bar by using the foreign body forcep. So we report it with review of literature.

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A Study on the Correlation among the Patterns of the Zone 1, 2, 3 of Factor AA in 7-Zone-Diagnostic System and the Clinical Parameters (7구역진단기의 Factor AA 제1, 2, 3구역 유형과 임상지표와의 상관성 연구)

  • Cho, Yi-Hyun;Yu, Jung-Suk;Lee, Hwi-Yong;Song, Beom-Yong
    • Journal of Acupuncture Research
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    • v.25 no.6
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    • pp.67-76
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    • 2008
  • Objectives : The 7-zone-diagnostic system is a diagnostic device to predetermine bodily locations by measuring the energy of body. This study was to investigate the relation between the different patterns of Zone 1, 2, 3 of Factor AA in CP-6000A(VEGA, Germany), 7-zone-diagnostic system and clinical parameters. The purpose of this study was relation Korean traditional medicine and western medicine with the data from 7-zone-diagnostic system and the clinical parameters. Methods : This study was carried out with the data from some clinical parameters. We made three groups according to the Factor AA patterns of CP-6000A. The Factor AA pattern of Group A is that the red bar graph of zone 1, 2, 3 were higher than the normal range and the others were the normal range. The Factor AA pattern of Group B was that the red bar graph of zone 1, 2, 3 was the normal range and the others were the normal range. The Factor AA pattern of Group C was that the red bar graph of zone 1, 2, 3 was lower than the normal range and the others were the normal range. After the data from clinical parameters to correspond with conditions of each group were selected, the data from clinical parameters among each groups analyzed statistically. Results : The values of GOT, GPT, r-GPT, Triglyceride, BUN, Uric acid of group A was higher than group C. Gastroscope of group A and B was higher than group C. Conclusions : It is thought that the red bar graph of zone 1, 2, 3 is higher, the group has the higher energy and the energy has a character of fire(熱). Those patterns have a high risk of hyperlipermia and liver, stomach disease.

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Evaluation of Transfer Learning in Gastroscopy Image Classification using Convolutional Neual Network (합성곱 신경망을 활용한 위내시경 이미지 분류에서 전이학습의 효용성 평가)

  • Park, Sung Jin;Kim, Young Jae;Park, Dong Kyun;Chung, Jun Won;Kim, Kwang Gi
    • Journal of Biomedical Engineering Research
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    • v.39 no.5
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    • pp.213-219
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    • 2018
  • Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.