Journal of Gastric Cancer

  • 제23권3호
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  • Pages.375-387
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  • 2023
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  • 2093-582X(pISSN)
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  • 2093-5641(eISSN)
대한위암학회 (The Korean Gastric Cancer Association)
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The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review

  • JunHo Lee (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center) ;
  • Hanna Lee (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center) ;
  • Jun-won Chung (Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center)
  • 투고 : 2023.07.07
  • 심사 : 2023.07.31
  • 발행 : 2023.07.31
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초록

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

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