Observational Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 7, 2022; 28(37): 5483-5493
Published online Oct 7, 2022. doi: 10.3748/wjg.v28.i37.5483
Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma
Qian-Qian Meng, Ye Gao, Han Lin, Tian-Jiao Wang, Yan-Rong Zhang, Jian Feng, Zhao-Shen Li, Lei Xin, Luo-Wei Wang
Qian-Qian Meng, Ye Gao, Han Lin, Tian-Jiao Wang, Yan-Rong Zhang, Zhao-Shen Li, Lei Xin, Luo-Wei Wang, Department of Gastroenterology, Changhai Hospital, Shanghai 200433, China
Jian Feng, Qingdao Medcare Digital Engineering Co. Ltd., Qingdao Medcare Digital Engineering Co. Ltd., Qingdao 26600, Shandong Province, China
Author contributions: Wang LW was the guarantor and designed the study; Lin H, Wang TJ, Zhang YR, Feng J participated in the acquisition, analysis, and interpretation of the data; Gao Y, Meng QQ drafted the initial manuscript; Xin L, Li ZS revised the article critically for important intellectual content; Meng QQ, Gao Y and Lin H contributed equally to this article.
Supported by Shanghai Science and Technology Innovation Action Program, No. 21Y31900100; and 234 Clinical Research Fund of Changhai Hospital, No. 2019YXK006.
Institutional review board statement: The study was reviewed and approved by the Shanghai Changhai Hospital Ethical Committee.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: Dr. Wang reports grants from The Science and Technology Commission of Shanghai Municipality during the conduct of the study. Other authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Luo-Wei Wang, MD, Chief Doctor, Department of Gastroenterology, Changhai Hospital, No. 168 Changhai Road, Yangpu District, Shanghai 200433, China. wangluoweimd@126.com
Received: June 2, 2022
Peer-review started: June 2, 2022
First decision: August 1, 2022
Revised: August 9, 2022
Accepted: September 20, 2022
Article in press: September 20, 2022
Published online: October 7, 2022
Processing time: 118 Days and 22.1 Hours
ARTICLE HIGHLIGHTS
Research background

Esophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related morbidity and mortality worldwide. Upper gastrointestinal endoscopy is critical for ESCC detection; however, endoscopists require long-term training to avoid missing superficial lesions. Artificial intelligence (AI) has been increasingly investigated to assist endoscopic diagnosis.

Research motivation

AI has shown promising results for endoscopic diagnosis of superficial ESCC. However, few AI-based computer-assisted diagnosis (CAD) systems for ESCC that support white-light and narrow-band imaging have been applied in clinical practice.

Research objectives

We aimed to develop a CAD system for endoscopic detection of superficial ESCC and investigate its application value.

Research methods

We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm. A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets, respectively. Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance. The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

Research results

The area under the receiver operating characteristics curve, accuracy, sensitivity, and specificity of the CAD system were 0.982 [95% confidence interval (CI): 0.969-0.994], 92.9% (95%CI: 89.5%-95.2%), 91.9% (95%CI: 87.4%-94.9%), and 94.7% (95%CI: 89.0%-97.6%), respectively. The accuracy of CAD was significantly higher than that of non-expert endoscopists (78.3%, P < 0.001 compared with CAD) and comparable to that of expert endoscopists (91.0%, P = 0.129 compared with CAD). After referring to the CAD results, the accuracy of the non-expert endoscopists significantly improved (88.2% vs 78.3%, P < 0.001).

Research conclusions

The diagnostic performance of the CAD system is promising and may assist in improving detectability, particularly for inexperienced endoscopists.

Research perspectives

An updated CAD system that can process real-time videos or images with suboptimal quality is in development. Randomized controlled trials are warranted to investigate the clinical pragmaticality.