Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
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DOI：
10.1016/j.gie.2018.10.027
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发表时间：
2019-02-01
影响因子：
7.7
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
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作者：
Aoki, Tomonori;Yamada, Atsuo;Tada, Tomohiro
背景和目标：尽管糜烂和溃疡是无线胶囊式内窥镜（WCE）发现的最常见的小肠异常，但尚未建立计算机辅助检测方法。我们的目标是开发一个具有深度学习的人工智能系统，以自动检测WCE图像中的糜烂和溃疡。方法：我们使用5360张糜烂和溃疡的WCE图像，训练了一个基于单次拍摄多盒检测器的深度卷积神经网络（CNN）系统。我们通过计算受试者工作特征曲线下的面积及其灵敏度、特异性和准确性来评估其性能，使用10，440张小肠图像的独立测试集，包括440张糜烂和溃疡图像。糜烂和溃疡检测的曲线下面积为0.958（95%置信区间[CI]，0.947-0.968）。CNN的敏感性、特异性和准确性均为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%），概率评分的临界值为0.481。我们开发并验证了一种基于CNN的新系统，可以自动检测WCE图像中的糜烂和溃疡。这可能是开发WCE图像日常诊断软件的关键一步，有助于减少疏忽和医生的负担。
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.