Application of artificial intelligence based on data enhancement and hybrid neural network to site identification during esophagogastroduodenoscopy
10.3760/cma.j.cn321463-20211019-00628
- VernacularTitle:基于数据增强和混合神经网络的人工智能技术在上消化道内镜检查部位识别中的应用
- Author:
Shixu WANG
1
;
Yan KE
;
Jiangtao CHU
;
Shun HE
;
Yueming ZHANG
;
Lizhou DOU
;
Yong LIU
;
Xudong LIU
;
Yumeng LIU
;
Hairui WU
;
Feixiong SU
;
Feng PENG
;
Meiling WANG
;
Fengying ZHANG
;
Lin WANG
;
Wei ZHANG
;
Guiqi WANG
Author Information
1. 国家癌症中心 国家肿瘤临床医学研究中心 中国医学科学院北京协和医学院肿瘤医院内镜科，北京 100021
- Keywords:
Artificial intelligence;
Deep convolutional neural network;
Esophagogastroduodenoscopy;
Sites identification
- From:
Chinese Journal of Digestive Endoscopy
2023;40(3):189-195
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.
