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

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

DCNN:

Deep convolutional neural network

Ha:

Harmonic mean

H. pylori :

Helicobacter pylori

PG:

Pepsinogen

ROC curve:

Receiver operating characteristic curve

ROI:

Region of interest

Se:

Sensitivity

Sp:

Specificity

UGI-ES:

Upper gastrointestinal endoscopy

UGI-XR:

Double-contrast upper gastrointestinal barium X-ray radiography

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Acknowledgements

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.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan

    Ren Togo, Kenta Ishihara, Takahiro Ogawa & Miki Haseyama

  2. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Nobutake Yamamichi, Yu Takahashi & Chihiro Takeuchi

  3. Department of Gastroenterology, National Hospital Organization Hakodate Hospital, 18-16, Kawahara-cho, Hakodate City, Hokkaido, 041-8512, Japan

    Katsuhiro Mabe & Mototsugu Kato

  4. Department of Gastroenterology, Hokkaido University Graduate School of Medicine, Sapporo, 060-8648, Japan

    Naoya Sakamoto

Authors
  1. Ren Togo
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  2. Nobutake Yamamichi
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  3. Katsuhiro Mabe
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  4. Yu Takahashi
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  7. Naoya Sakamoto
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  8. Kenta Ishihara
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  9. Takahiro Ogawa
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  10. Miki Haseyama
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Corresponding author

Correspondence to Katsuhiro Mabe.

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Togo, R., Yamamichi, N., Mabe, K. et al. Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J Gastroenterol 54, 321–329 (2019). https://doi.org/10.1007/s00535-018-1514-7

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  • DOI: https://doi.org/10.1007/s00535-018-1514-7

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