Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system

Abstract

Background

The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist’s Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification.

Materials and Methods

A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared.

Results

12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists.

Conclusions

The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

KEY MESSAGES

• A new attention mechanism module has been proposed and integrated into the DL model.

• According to our knowledge, this is the first study to establish a four-category DL model based on the Hill grading.

• The DL model demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

Keywords:

• Gastroesophageal flap valve
• classification;attention mechanism;deep learning
• endoscopy

Acknowledgments

We appreciate all coworkers from the Digestive Endoscopy Center of Dalian Municipal Central Hospital and Dalian Central Laboratory of Integrative Neuro-gastrointestinal Dynamics and Metabolism Related Diseases Prevention and Treatment for their assistance.

Ethical approval and consent to participate

This research was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Dalian Municipal Central Hospital (Approval No. YN2021-065-01). Patient consent was waived due to the non-interventional retrospective design of the study.

Authors’ contributions

Zhijun Duan, Xin Yang, and Jiuyang Chang designed the study; Zhenyang Ge and Youjiang Fang drafted the manuscript; Zhenyang Ge analyzed the data; Youjiang Fang and Yu Qiao provided technical support; Zhijun Duan and Xin Yang revised the manuscript; Jiuyang Chang, Yu Qiao, Zequn Yu, and Jing Zhang contributed to the interpretation of the 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.

Additional information

Funding

This research was financially supported by the Dalian Innovation and Entrepreneurship Support Program for Chinese Returned Scholars (No. RSC001).