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
Abstract
BACKGROUND

A convolutional neural network (CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.

AIM

To automatically identify the invasion depth and origin of esophageal lesions based on a CNN.

METHODS

A total of 1670 white-light images were used to train and validate the CNN system. The method proposed in this paper included the following two parts: (1) Location module, an object detection network, locating the classified main image feature regions of the image for subsequent classification tasks; and (2) Classification module, a traditional classification CNN, classifying the images cut out by the object detection network.

RESULTS

The CNN system proposed in this study achieved an overall accuracy of 82.49%, sensitivity of 80.23%, and specificity of 90.56%. In this study, after follow-up pathology, 726 patients were compared for endoscopic pathology. The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%; 41 patients showed no lesion invasion to the muscularis propria, but 36 of them pathologically showed invasion to the superficial muscularis propria. The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%. For the examination of submucosal lesions, the accuracy of endoscopic ultrasonography (EUS) was approximately 99.3%. Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions, whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions. Misdiagnosis could be due to different operating and diagnostic levels of endoscopists, unclear ultrasound probes, and unclear lesions.

CONCLUSION

This study is the first to recognize esophageal EUS images through deep learning, which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors, thereby achieving good accuracy. In future studies, this method can provide guidance and help to clinical endoscopists.

Keywords: Endoscopic ultrasonography; Convolutional neural network; Esophageal lesion; Automatically; Classify; Identify

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.