A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images

Abstract

Background

Chronic atrophic gastritis is a common preneoplastic condition of the stomach with a low detection rate during endoscopy.

Aims

This study aimed to develop two deep learning models to improve the diagnostic rate.

Methods

We collected 10,593 images from 4005 patients including 2280 patients with chronic atrophic gastritis and 1725 patients with chronic non-atrophic gastritis from two tertiary hospitals. Two deep learning models were developed to detect chronic atrophic gastritis using ResNet50. The detection ability of the deep learning model was compared with that of three expert endoscopists.

Results

In the external test set, the diagnostic accuracy of model 1 for detecting gastric antrum atrophy was 0.890. The identification accuracies for the severity of gastric antrum atrophy were 0.773 and 0.590 in the internal and external test sets, respectively. In the other two external sets, the detection accuracies of model 2 for chronic atrophic gastritis were 0.854 and 0.916, respectively. Deep learning model 1′s ability to identify gastric antrum atrophy was comparable to that of human experts.

Conclusion

Deep-learning-based models can detect chronic atrophic gastritis with good performance, which may greatly reduce the burden on endoscopists, relieve patient suffering, and improve the disease's detection rate in primary hospitals.