Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 7, 2020; 26(25): 3650-3659
Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3650
Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama
Misaki Kanai, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 0600814, Hokkaido, Japan
Ren Togo, Education and Research Center for Mathematical and Data Science, Hokkaido University, Sapporo 0600812, Hokkaido, Japan
Takahiro Ogawa, Miki Haseyama, Faculty of Information Science and Technology, Hokkaido University, Sapporo 0600814, Hokkaido, Japan
Author contributions: Kanai M and Togo R wrote the paper; Kanai M performed the majority of experiments and analyzed the data; Ogawa T and Haseyama M designed and coordinated the research.
Supported by JSPS KAKENHI Grant, No. JP17H01744.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of The University of Tokyo Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous data that were obtained after each patient agreed to inspections by written consent.
Conflict-of-interest statement: The authors have no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Ren Togo, Education and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-Ku, Sapporo 0600812, Hokkaido, Japan.
Received: February 10, 2020
Peer-review started: February 10, 2020
First decision: March 15, 2020
Revised: April 3, 2020
Accepted: June 18, 2020
Article in press: June 18, 2020
Published online: July 7, 2020
Research background

It has been reported that chronic atrophic gastritis (CAG) induce by Helicobacter pylori infection increases the risk of gastric cancer. X-ray examination can evaluate the condition of the stomach for mass screening. On the other hand, there remains a problem that skilled doctors are decreasing.

Research motivation

Researches for the detection of CAG have been conducted, especially, deep learning-based techniques have achieved high recognition performance in general image datasets. However, early works need a large number of labeled images for training.

Research objectives

The study aimed to evaluate the effectiveness of a deep learning technique with a small number of training images with the stomach region annotation.

Research methods

A total of 815 gastric X-ray images (GXIs) were used in our analysis. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A deep learning model is trained with the stomach region annotations. For the rest of them, the stomach regions are automatically estimated by the learned model. Finally, a model for automatic CAG detection is trained with all GXIs for training.

Research results

In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively.

Research conclusions

By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.

Research perspectives

Our CAG detection method can be trained with data from a small-scale or medium-scale hospital without medical data sharing that having the risk of leakage of personal information.