Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 7, 2022; 28(41): 5931-5943
Published online Nov 7, 2022. doi: 10.3748/wjg.v28.i41.5931
Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning
Jun-Xiao Zhou, Zhan Yang, Ding-Hao Xi, Shou-Jun Dai, Zhi-Qiang Feng, Jun-Yan Li, Wei Xu, Hong Wang
Jun-Xiao Zhou, Shou-Jun Dai, Zhi-Qiang Feng, Jun-Yan Li, Hong Wang, Department of Gastroenterology and Hepatology, Guangzhou First People’s Hospital, Guangzhou 510180, Guangdong Province, China
Zhan Yang, Ding-Hao Xi, Wei Xu, School of Information, Renmin University of China, Beijing 100872, China
Author contributions: Zhou JX collected and compiled the data; Yang Z and Xi DH were responsible for coding the model; Zhou JX, Yang Z, and Li J drafted the manuscript; Dai SJ, Feng ZQ, and Wang H performed the capsule endoscopy examinations and reviewed the images; Xu W and Wang H contributed to the study design and manuscript writing.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Guangzhou First People’s Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
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: Hong Wang, MD, Chief Physician, Department of Gastroenterology and Hepatology, Guangzhou First People’s Hospital, No. 1 Panfu Road, Yuexiu District, Guangzhou 510180, Guangdong Province, China. wong.hong@163.com
Received: June 29, 2022
Peer-review started: June 29, 2022
First decision: August 19, 2022
Revised: August 31, 2022
Accepted: October 19, 2022
Article in press: October 19, 2022
Published online: November 7, 2022
Processing time: 127 Days and 14.1 Hours
Core Tip

Core Tip: Artificial intelligence has been widely used in capsule endoscopy to detect gastrointestinal polyps; however, it is often impaired by artifacts in clinical practice. At present, clear and high-quality images without artifacts are usually selected for research, which has not yet produced practical assistance regarding artifact interference. In this study, we demonstrated that ensemble learning can improve the segmentation performance of polyps under the interference of artifacts, which has a significant auxiliary role in the detection of polyps in clinical practice.