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CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(06): E955-E964
DOI: 10.1055/a-1372-2789
Original article

Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial

Ganggang Mu‡
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Yijie Zhu‡
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Zhanyue Niu‡
4   Peking University Third Hospital, Beijing, China
,
Hongyan Li
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Lianlian Wu
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Jing Wang
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Renquan Luo
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Xiao Hu
5   Wuhan EndoAngel Medical Technology Company, Wuhan, China
,
Yanxia Li
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Jixiang Zhang
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Shan Hu
5   Wuhan EndoAngel Medical Technology Company, Wuhan, China
,
Chao Li
5   Wuhan EndoAngel Medical Technology Company, Wuhan, China
,
Shigang Ding‡   **
4   Peking University Third Hospital, Beijing, China
,
Honggang Yu‡   **
1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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Abstract

Background and study aims Endoscopy plays a crucial role in diagnosis of gastritis. Endoscopists have low accuracy in diagnosing atrophic gastritis with white-light endoscopy (WLE). High-risk factors (such as atrophic gastritis [AG]) for carcinogenesis demand early detection. Deep learning (DL)-based gastritis classification with WLE rarely has been reported. We built a system for improving the accuracy of diagnosis of AG with WLE to assist with this common gastritis diagnosis and help lessen endoscopist fatigue.

Methods We collected a total of 8141 endoscopic images of common gastritis, other gastritis, and non-gastritis in 4587 cases and built a DL -based system constructed with UNet + + and Resnet-50. A system was developed to sort common gastritis images layer by layer: The first layer included non-gastritis/common gastritis/other gastritis, the second layer contained AG/non-atrophic gastritis, and the third layer included atrophy/intestinal metaplasia and erosion/hemorrhage. The convolutional neural networks were tested with three separate test sets.

Results Rates of accuracy for classifying non-atrophic gastritis/AG, atrophy/intestinal metaplasia, and erosion/hemorrhage were 88.78 %, 87.40 %, and 93.67 % in internal test set, 91.23 %, 85.81 %, and 92.70 % in the external test set ,and 95.00 %, 92.86 %, and 94.74 % in the video set, respectively. The hit ratio with the segmentation model was 99.29 %. The accuracy for detection of non-gastritis/common gastritis/other gastritis was 93.6 %.

Conclusions The system had decent specificity and accuracy in classification of gastritis lesions.DL has great potential in WLE gastritis classification for assisting with achieving accurate diagnoses after endoscopic procedures.

** These authors contributed equally to this work.


Supplementary material



Publication History

Received: 17 September 2020

Accepted: 14 December 2020

Article published online:
27 May 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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