Clinical Endoscopy

  • 제57권3호
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  • Pages.283-292
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  • 2024
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  • 2234-2400(pISSN)
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  • 2234-2443(eISSN)
대한소화기내시경학회 (The Korean Society of Gastrointestinal Endoscopy)
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E-learning system to improve the endoscopic diagnosis of early gastric cancer

  • Kenshi Yao (Department of Endoscopy, Fukuoka University Chikushi Hospital) ;
  • Takashi Yao (Department of Human Pathology, Juntendo University Graduate School of Medicine) ;
  • Noriya Uedo (Department of Gastrointestinal Oncology, Osaka International Cancer Institute) ;
  • Hisashi Doyama (Department of Gastroenterology, Ishikawa Prefectural Central Hospital) ;
  • Hideki Ishikawa (Department of Molecular-Targeting Cancer Prevention, Graduate School of Medical Science, Kyoto Prefectural University of Medicine) ;
  • Satoshi Nimura (Department of Pathology, Fukuoka University Chikushi Hospital) ;
  • Yuichi Takahashi (Department of Development, FAST Inc.)
  • 투고 : 2023.03.14
  • 심사 : 2023.04.23
  • 발행 : 2024.05.30
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초록

We developed three e-learning systems for endoscopists to acquire the necessary skills to improve the diagnosis of early gastric cancer (EGC) and demonstrated their usefulness using randomized controlled trials. The subjects of the three e-learning systems were "detection", "characterization", and "preoperative assessment". The contents of each e-learning system included "technique", "knowledge", and "obtaining experience". All e-learning systems proved useful for endoscopists to learn how to diagnose EGC. Lecture videos describing "the technique" and "the knowledge" can be beneficial. In addition, repeating 100 self-study cases allows learners to gain "experience" and improve their diagnostic skills further. Web-based e-learning systems have more advantages than other teaching methods because the number of participants is unlimited. Histopathological diagnosis is the gold standard for the diagnosis of gastric cancer. Therefore, we developed a comprehensive diagnostic algorithm to standardize the histopathological diagnosis of gastric cancer. Once we have successfully shown that this algorithm is helpful for the accurate histopathological diagnosis of cancer, we will complete a series of e-learning systems designed to assess EGC accurately.

키워드

과제정보

We thank Honyaku Center Inc. for English language editing. The authors received no financial support for this study.

참고문헌

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