Kanai M, Togo R, Ogawa T, Haseyama M. Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions. World J Gastroenterol 2020; 26(25): 3650-3659 [PMID: 32742133 DOI: 10.3748/wjg.v26.i25.3650]
Corresponding Author of This Article
Ren Togo, Education and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-Ku, Sapporo 0600812, Hokkaido, Japan. togo@lmd.ist.hokudai.ac.jp
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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 byJSPS 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: http://creativecommons.org/licenses/by-nc/4.0/
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. togo@lmd.ist.hokudai.ac.jp
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 Processing time: 141 Days and 11.3 Hours
ARTICLE HIGHLIGHTS
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.
