Background and Aims

GI angiectasia (GIA) is the most common small-bowel (SB) vascular lesion, with an inherent risk of bleeding. SB capsule endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a computer-assisted diagnosis tool for the detection of GIA.

Methods

Deidentified SB-CE still frames featuring annotated typical GIA and normal control still frames were selected from a database. A semantic segmentation images approach associated with a convolutional neural network (CNN) was used for deep-feature extractions and classification. Two datasets of still frames were created and used for machine learning and for algorithm testing.

Results

The GIA detection algorithm yielded a sensitivity of 100%, a specificity of 96%, a positive predictive value of 96%, and a negative predictive value of 100%. Reproducibility was optimal. The reading process for an entire SB-CE video would take 39 minutes.

Conclusions

The developed CNN-based algorithm had high diagnostic performances, allowing detection of GIA in SB-CE still frames. This study paves the way for future automated CNN-based SB-CE reading softwares.

中文翻译：

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小肠胶囊内镜检查中胃肠道血管扩张的神经网络算法

背景和目标

GI血管扩张（GIA）是最常见的小肠（SB）血管病变，具有内在的出血风险。SB胶囊内窥镜检查（SB-CE）是目前公认的诊断程序。这项研究的目的是开发一种用于检测GIA的计算机辅助诊断工具。

方法

从数据库中选择了带有注释的典型GIA和标准控制静态帧的已标识SB-CE静态帧。与卷积神经网络（CNN）相关的语义分割图像方法用于深度特征提取和分类。创建了两个静止帧数据集，并将其用于机器学习和算法测试。

结果

GIA检测算法得出的灵敏度为100％，特异性为96％，阳性预测值为96％，阴性预测值为100％。重现性最佳。整个SB-CE视频的阅读过程将花费39分钟。

结论

所开发的基于CNN的算法具有很高的诊断性能，可以检测SB-CE静止帧中的GIA。这项研究为将来的基于CNN的自动化SB-CE阅读软件铺平了道路。