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Artificial intelligence for the detection of gastric precancerous conditions using image-enhanced endoscopy: What kind of abilities are required for application in real-world clinical practice?

      The term “artificial intelligence” (AI) is used to describe machines that think like humans. It was coined by the computer scientist John McCarthy at the 1956 Dartmouth workshop in the United States. Although there is no fixed definition, AI is the simulation of human intelligence processes by machines, especially computer systems. Image recognition, a type of pattern recognition technology that uses the features of images and videos to identify objects, is one field of AI research. AI image recognition has advanced significantly since the development of convolutional neural networks (CNN), a typical method of deep learning (a type of machine learning). Deep learning exhibited dramatic progress over conventional methods such as Bag-of-Visual Words at the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a major AI image recognition contest. In 2015, AI achieved a lower error rate than did humans, demonstrating its greater image recognition capacity. After improvements in computer performance, many reports mentioned the use of AI to support software for diagnostic imaging, particularly in the fields of radiology,
      • Bibault J.E.
      • Giraud P.
      • Burgun A.
      Big data and machine learning in radiation oncology: state of the art and future prospects.
      dermatology,
      • Esteva A.
      • Kuprel B.
      • Novoa R.A.
      • et al.
      Dermatologist-level classification of skin cancer with deep neural networks.
      and pathology.
      • Yoshida H.
      • Shimazu T.
      • Kiyuna T.
      • et al.
      Automated histological classification of whole-slide images of gastric biopsy specimens.

      Abbreviations:

      AI (artificial intelligence), CADe (computer-aided detection), CNN (convolutional neural networks), ME-NBI/BLI (magnifying endoscopy with narrow-band imaging/blue laser imaging), NBI (narrow-band imaging)
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      References

        • Bibault J.E.
        • Giraud P.
        • Burgun A.
        Big data and machine learning in radiation oncology: state of the art and future prospects.
        Cancer Lett. 2016; 382: 110-117
        • Esteva A.
        • Kuprel B.
        • Novoa R.A.
        • et al.
        Dermatologist-level classification of skin cancer with deep neural networks.
        Nature. 2017; 542: 115-118
        • Yoshida H.
        • Shimazu T.
        • Kiyuna T.
        • et al.
        Automated histological classification of whole-slide images of gastric biopsy specimens.
        Gastric Cancer. 2018; 21: 249-257
        • Luo H.
        • Xu G.
        • Li C.
        • et al.
        Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.
        Lancet Oncol. 2019; 20: 1645-1654
        • Byrne M.F.
        • Chapados N.
        • Soudan F.
        • et al.
        Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
        Gut. 2019; 68: 94-100
        • Zhu Y.
        • Wang Q.C.
        • Xu M.D.
        • et al.
        Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.
        Gastrointest Endosc. 2019; 89: 806-815
        • Xu M.
        • Zhou W.
        • Wu L.
        • et al.
        Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video).
        Gastrointest Endosc. 2021; 94: 540-548.e4
        • Kikuste I.
        • Marques P.R.
        • Monteiro S.M.
        • et al.
        Systematic review of the diagnosis of gastric premalignant conditions and neoplasia with high-resolution endoscopic technologies.
        Scand J Gastroenterol. 2013; 48: 1108-1117
        • Tsai T.L.
        • Fridsma D.B.
        • Gatti G.
        Computer decision support as a source of interpretation error: the case of electrocardiograms.
        J Am Med Informatics Assoc. 2003; 10: 478-483
        • Mori Y.
        • Kudo S.E.
        • Misawa M.
        • et al.
        Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study.
        Ann Intern Med. 2018; 169: 357-366

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