In Scandinavian journal of gastroenterology
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.
Ge Zhenyang, Wang Bowen, Chang Jiuyang, Yu Zequn, Zhou Zhenyuan, Zhang Jing, Duan Zhijun
2023-Jan-09
Gastroesophageal reflux disease, classification, deep learning, endoscopy, explainable artificial intelligence
