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Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2018-10-25 , DOI: 10.1016/j.gie.2018.10.027
Tomonori Aoki , Atsuo Yamada , Kazuharu Aoyama , Hiroaki Saito , Akiyoshi Tsuboi , Ayako Nakada , Ryota Niikura , Mitsuhiro Fujishiro , Shiro Oka , Soichiro Ishihara , Tomoki Matsuda , Shinji Tanaka , Kazuhiko Koike , Tomohiro Tada

Background and Aims

Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an artificial intelligence system with deep learning to automatically detect erosions and ulcerations in WCE images.

Methods

We trained a deep convolutional neural network (CNN) system based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations.

Results

The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score.

Conclusions

We developed and validated a new system based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.



中文翻译:

基于深度卷积神经网络的无线胶囊内窥镜图像中侵蚀和溃疡的自动检测

背景和目标

尽管侵蚀和溃疡是无线胶囊内窥镜检查(WCE)上最常见的小肠异常,但尚未建立计算机辅助的检测方法。我们旨在开发一种具有深度学习的人工智能系统,以自动检测WCE图像中的侵蚀和溃疡。

方法

我们使用5360 WCE侵蚀和溃疡的图像训练了基于Single Shot Multibox Detector的深度卷积神经网络(CNN)系统。我们使用10,440张小肠图像(包括440张糜烂和溃疡图像)的独立测试集,通过计算接收器工作特性曲线下的面积及其灵敏度,特异性和准确性来评估其性能。

结果

经过训练的CNN需要233秒才能评估10,440张测试图像。用于检测糜烂和溃疡的曲线下面积为0.958(95%置信区间[CI],0.947-0.968)。CNN的敏感性,特异性和准确性分别为88.2%(95%CI,84.8%-91.0%),90.9%(95%CI,90.3%-91.4%)和90.8%(95%CI,90.2%- 91.3%),概率得分的截断值为0.481。

结论

我们开发并验证了基于CNN的新系统,该系统可自动检测WCE图像中的糜烂和溃疡。这可能是开发用于WCE图像的日常使用诊断软件的关键步骤,以帮助减少监督和减轻医生负担。

更新日期:2018-10-25
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