Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using deep convolutional neural network：a multicenter retrospective study (with video).,Gastrointestinal Endoscopy

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Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using deep convolutional neural network：a multicenter retrospective study (with video).
Gastrointestinal Endoscopy ( IF 6.7 ) Pub Date : 2020-06-26 , DOI: 10.1016/j.gie.2020.06.058
Mingkai Chen ₁ , Jing Wang ₁ , Yong Xiao ₁ , Lianlian Wu ₁ , Shan Hu ₂ , Shi Chen ₃ , Guodong Yi ₄ , Wei Hu ₅ , Xianmu Xie ₆ , Yijie Zhu ₁ , Yiyun Chen ₂ , Yanning Yang ₇ , Honggang Yu ₁

Affiliation

Background and Aims

Rupture of gastroesophageal varices is the most common fatal adverse event of cirrhosis. EGD is considered the criterion standard for diagnosis and risk stratification of gastroesophageal variceal bleeding. The aim of this study was to train and validate a real-time deep convolutional neural network (DCNN) system, named ENDOANGEL, for diagnosing gastroesophageal varices and predicting the risk of rupture.

Methods

After training with 8566 images of endoscopic gastroesophageal varices from 3021 patients and 6152 images of normal esophagus/stomach from 3168 patients, ENDOANGEL was also tested with independent images and videos. It was also compared with endoscopists in several aspects.

Results

ENDOANGEL, in contrast with endoscopists, displayed higher accuracy of 97.00% and 92.00% in terms of detecting esophageal varices (EVs) and gastric varices (GVs) in an image contest (97.00% vs 93.94% , P < .01; 92.00% vs 84.43%, P < .05). It also surpassed endoscopists for red color signs of EVs and red spots of GVs (84.21% vs 73.45%, P < .01; 85.26% vs 77.52%, P < .05). Moreover, ENDOANGEL achieved comparable performance in the determination of size, form, color, and bleeding signs. ENDOANGEL also had good performance in making treatment suggestions. With regard to predicting risk factors in multicenter videos, ENDOANGEL showed great stability.

Conclusions

Our data suggest that DCNNs were precise in detecting both EVs and GVs and performed excellently in uncovering the endoscopic risk factors of gastroesophageal variceal bleeding. Thus, the application of DCNNs will assist endoscopists in evaluating gastroesophageal varices more objectively and precisely. (Clinical trial registration number: ChiCTR1900023970.)

中文翻译：

食管胃十二指肠镜下使用深度卷积神经网络自动实时地验证胃食管静脉曲张：多中心回顾性研究（带视频）。

背景和目标

胃食管静脉曲张破裂是肝硬化最常见的致命不良事件。EGD被认为是胃食管静脉曲张破裂出血的诊断和危险分层的标准标准。这项研究的目的是训练和验证名为ENDOANGEL的实时深层卷积神经网络（DCNN）系统，以诊断胃食管静脉曲张和预测破裂风险。

方法

在对3021例患者的8566例胃镜下胃食管静脉曲张图像和3168例患者的6152例正常食管/胃图像进行训练后，还对ENDOANGEL进行了独立的图像和视频测试。还与内镜医师在多个方面进行了比较。

结果

与内镜医师相比，ENDOANGEL在图像竞赛中检测食管静脉曲张（EV）和胃静脉曲张（GV）时显示出更高的准确性，分别为97.00％和92.00％（97.00％对93.94％，P <.01； 92.00％对84.43％，P <.05）。EV的红色标志和GV的红色斑点也超过了内镜检查员（84.21％对73.45％，P <.01； 85.26％对77.52％，P <.05）。此外，ENDOANGEL在确定大小，形式，颜色和出血迹象方面也取得了可比的性能。ENDOANGEL在提出治疗建议方面也表现良好。关于预测多中心视频中的危险因素，ENDOANGEL显示出很好的稳定性。