Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System

Abstract

Objectives

Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI).

Materials and methods

A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared.

Results

Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications.

Conclusions

The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.

Keywords:

• Gastroesophageal reflux disease
• deep learning
• explainable artificial intelligence
• classification
• endoscopy

Acknowledgments

We appreciate all the coworkers from the Digestive Endoscopy Center of Dalian Municipal Central Hospital for providing data. This research received no external funding.

Author contributions

Zhijun Duan, Jiuyang Chang, and Jing Zhang designed the study; Zhenyang Ge and Bowen Wang drafted the manuscript; Zhenyang Ge analyzed the data; Bowen Wang provided technical support; Zhijun Duan, and Jing Zhang revised the manuscript; Jiuyang Chang, Zhenyuan Zhou, and Zequn Yu contributed to the interpretation of data. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data presented in this study are available on request from the corresponding author.