Journal of Gastroenterology

, Volume 54, Issue 4, pp 321–329 | Cite as

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

  • Ren Togo
  • Nobutake Yamamichi
  • Katsuhiro MabeEmail author
  • Yu Takahashi
  • Chihiro Takeuchi
  • Mototsugu Kato
  • Naoya Sakamoto
  • Kenta Ishihara
  • Takahiro Ogawa
  • Miki Haseyama
Original Article—Alimentary Tract



Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography.


A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification.


Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method.


Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.


Deep convolutional neural network Artificial intelligence Gastritis Double-contrast upper gastrointestinal barium X-ray radiography 



Deep convolutional neural network


Harmonic mean

H. pylori

Helicobacter pylori



ROC curve

Receiver operating characteristic curve


Region of interest






Upper gastrointestinal endoscopy


Double-contrast upper gastrointestinal barium X-ray radiography



The clinical data were acquired at The University of Tokyo Hospital in Japan. This study was partly supported by Global Station for Big Data and Cybersecurity, a project of Global Institution for Collaborative Research and Education at Hokkaido University JSPS KAKENHI Grant number JP17H01744.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest


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Copyright information

© Japanese Society of Gastroenterology 2018

Authors and Affiliations

  • Ren Togo
    • 1
  • Nobutake Yamamichi
    • 2
  • Katsuhiro Mabe
    • 3
    Email author
  • Yu Takahashi
    • 2
  • Chihiro Takeuchi
    • 2
  • Mototsugu Kato
    • 3
  • Naoya Sakamoto
    • 4
  • Kenta Ishihara
    • 1
  • Takahiro Ogawa
    • 1
  • Miki Haseyama
    • 1
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan
  2. 2.Department of Gastroenterology, Graduate School of MedicineThe University of TokyoTokyoJapan
  3. 3.Department of GastroenterologyNational Hospital Organization Hakodate HospitalHakodate CityJapan
  4. 4.Department of GastroenterologyHokkaido University Graduate School of MedicineSapporoJapan

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