Computer-aided Diagnosis of Helicobacter pylori Infection Using Linked-color Imaging and X-ray Images Hirotaka Nakashima 1 , Hiroshi Kawahira 2 , Ryo Shigematsu 3 , Chika Fukuyama 1 , Meiko Shimoi 1 , Naoko Kitazawa 1 , Kumiko Momma 1 , Nobuhiro Sakaki 1 1Foundation for Detection of Early Gastric Carcinoma, Tokyo 2Medical Simulation Center, Jichi Medical University, Tochigi, Japan 3Department of Radiology, Genki Plaza Medical Center for Health Care, Tokyo Keyword: AI診断 , H. pylori感染 , 内視鏡診断 , 画像強調内視鏡 , X線診断 pp.473-480
Published Date 2021/4/25
DOI https://doi.org/10.11477/mf.1403202303
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 The diagnosis of H. pylori(Helicobacter pylori)infection by clinical imaging can be effective in stratifying the risk of gastric cancer. In this article, we describe our results of AI(artificial intelligence)using a computer image recognition technology for the diagnosis of H. pylori infection. The accuracy of LCI-CAD(computer-aided diagnosis using linked-color imaging)was 84.1% in uninfected individuals, 81.7% in currently infected patients, and 78.6% in post-eradication cases. The diagnostic accuracy based on the LCI-CAD data was superior to that based on the white light imaging CAD data in the uninfected, currently infected, and post-eradication cases. In addition, the diagnostic accuracy of LCI-CAD was comparable with those of experienced endoscopists. Furthermore, in this study, a CAD system was created using gastric X-ray double-contrast images. The X-ray CAD system showed a sensitivity of 86.7% and specificity of 91.7%. The use of AI for the diagnosis of H. pylori is associated with both a superior level of diagnostic accuracy and high diagnostic speed. In addition, it is possible to duplicate the program. Hence, we believe that the application of this method for the stratification of gastric cancer risk will provide diagnostic support for endoscopic and X-ray screening programs in patients with early gastric cancer.

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