Liu GS, Huang PY, Wen ML, Zhuang SS, Hua J, He XP. Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network. World J Gastroenterol 2022; 28(22): 2457-2467 [PMID: 35979257 DOI: 10.3748/wjg.v28.i22.2457]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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 bythe 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
ARTICLE HIGHLIGHTS
Research background
The esophagus is the narrowest part of the digestive tract and its diameter is not uniform. Endoscopic ultrasonography (EUS) can be used for high-precision evaluation of the esophagus. With the popularization of endoscopic ultrasonography in clinic, more and more endoscopic physicians are needed. The rapidly evolving field of machine learning is key to this paradigm shift.
Research motivation
Endoscopic ultrasonography is different from other ultrasound modalities in that ultrasonic examination is performed on the basis of digestive endoscopy, so the examiners should be professional endoscopic physicians with ultrasonic training. And as we face the interplay of increasing chronic diseases, ageing populations, and dwindling resources, we need to shift to models that can intelligently extract, analyze, interpret, and understand increasingly complex data. However, the research on the identification and classification of esophageal lesions by endoscopic ultrasonography has not yet been found.
Research objectives
The purpose of this study was to construct a framework of deep learning network to study the application of deep learning in esophageal EUS in identifying the origin of submucosal lesions and defining the scope of esophageal lesions.
Research methods
A total of 1670 white-light images were used to train and validate the convolutional neural network (CNN) system. In the study, VGGNet was used to perform classification tasks, and multiple superimposed filters were used to increase the nonlinearity of the whole function and reduce the number of parameters.
Research results
A total of 1115 patients were included in this analysis, including 694 males and 421 females. The overall accuracy, sensitivity, and specificity were 82.49%, 80.23%, and 90.56% respectively. The images of lesions originating from the muscularis mucosa were easily confused with the images of lesions invading the muscularis mucosa and submucosa.
Research conclusions
This study constructed a CNN system which can automatically identify the lesion invasion depth and the lesion source of submucosal tumors, and classify them, achieving good accuracy.
Research perspectives
In the future of medicine, artificial intelligence will reduce the workload of medical staff and make targeted tests more accurate, and in future studies, it can provide guidance and help to clinical endoscopists.
