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. 2021 Jun;9(6):E955-E964.
doi: 10.1055/a-1372-2789. Epub 2021 May 27.

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

Ganggang Mu  1   2   3 , Yijie Zhu  1   2   3 , Zhanyue Niu  4 , Hongyan Li  1   2   3 , Lianlian Wu  1   2   3 , Jing Wang  1   2   3 , Renquan Luo  1   2   3 , Xiao Hu  5 , Yanxia Li  1   2   3 , Jixiang Zhang  1   2   3 , Shan Hu  5 , Chao Li  5 , Shigang Ding  4 , Honggang Yu  1   2   3
Affiliations

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

Ganggang Mu et al. Endosc Int Open. 2021 Jun.

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.

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Conflict of interest statement

Competing interests Drs. Shan Hu, Xiao Hu, and Chao Li are research staff members of Wuhan EndoAngel Medical Technology Company.

Figures

Fig. 1
Fig. 1
Structure of our system.
Fig. 2
Fig. 2
Representative images of four kinds of gastritis lesions. a Atrophy. b IM. c Erosion. d Hemorrhage. The first column are originals. The second are segmentation masks. The third shows: green dotted line and white box domain surrounds CNN predicting domain and blue dotted line and red box surrounds manual descripting domain. The fourth are demonstration: classification results and confidence are displayed.
Fig. 3
Fig. 3
Flowchart.
Fig. 4
Fig. 4
Performance of endoscopists vs. model. a , b , c Accuracy of CNNs and endoscopists in the internal, external, and video test sets, respectively.
Fig. 5
Fig. 5
Summary chart.
See this image and copyright information in PMC

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