Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 14, 2022; 28(22): 2457-2467
Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2457
Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network
Gao-Shuang Liu, Pei-Yun Huang, Min-Li Wen, Shuai-Shuai Zhuang, Jie Hua, Xiao-Pu He
Gao-Shuang Liu, Department of Gastroenterology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
Pei-Yun Huang, Shuai-Shuai Zhuang, Xiao-Pu He, Department of Geriatric Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
Min-Li Wen, School of Computer Science and Engineering, Southeast University, Nanjing 211102, Jiangsu Province, China
Jie Hua, Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
Author contributions: Liu GS and He XP proposed the conception and design; He XP and Hua J were responsible for administrative support; Hua J and Huang PY provided the study materials or patients; Liu GS, He XP, Huang PY, and Zhuang SS collected and compiled the data; Wen ML did the data analysis and interpretation; all authors participated in manuscript writing and approved the final version of manuscript.
Supported by the Natural Science Foundation of Jiangsu, No. BK20171508.
Institutional review board statement: The study was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (No. 2019-SR-448).
Informed consent statement: Informed consent for upper gastrointestinal endoscopy (UGE) was obtained from all cases.
Conflict-of-interest statement: The authors have no conflicts of interest to disclose.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jie Hua, MD, Chief Physician, Doctor, Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing 210000, Jiangsu Province, China. huajie@njmu.edu.cn
Received: April 27, 2021
Peer-review started: April 28, 2021
First decision: June 13, 2021
Revised: June 27, 2021
Accepted: April 29, 2022
Article in press: April 29, 2022
Published online: June 14, 2022
Processing time: 408 Days and 16.5 Hours
Core Tip

Core Tip: Convolutional neural networks with self-learning abilities are an effective method in medical image classification, segmentation, and detection. Endoscopic ultrasonography plays an important role in the diagnosis and treatment of esophageal lesions. However, its operation and lesion identification are more difficult than ordinary endoscopy. Automatic identification technology is of great significance to its development.