Published online Jun 14, 2022. doi: 10.3748/wjg.v28.i22.2457
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
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.
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.
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.
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.
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.
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.
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.