Background and Aims

Evaluation of endoscopic disease activity for patients with ulcerative colitis (UC) is important when determining the treatment of choice. However, endoscopists require a certain period of training to evaluate the activity of inflammation properly, and interobserver variability exists. Therefore, we constructed a computer-assisted diagnosis (CAD) system using a convolutional neural network (CNN) and evaluated its performance using a large dataset of endoscopic images from patients with UC.

Methods

A CNN-based CAD system was constructed based on GoogLeNet architecture. The CNN was trained using 26,304 colonoscopy images from a cumulative total of 841 patients with UC, which were tagged with anatomic locations and Mayo endoscopic scores. The performance of the CNN in identifying normal mucosa (Mayo 0) and mucosal healing state (Mayo 0–1) was evaluated in an independent test set of 3981 images from 114 patients with UC, by calculating the areas under the receiver operating characteristic curves (AUROCs). In addition, AUROCs in the right side of the colon, left side of the colon, and rectum were evaluated.

Results

The CNN-based CAD system showed a high level of performance with AUROCs of 0.86 and 0.98 to identify Mayo 0 and 0–1, respectively. The performance of the CNN was better for the rectum than for the right side and left side of the colon when identifying Mayo 0 (AUROC = 0.92, 0.83, and 0.83, respectively).

Conclusions

The performance of the CNN-based CAD system was robust when used to identify endoscopic inflammation severity in patients with UC, highlighting its promising role in supporting less-experienced endoscopists and reducing interobserver variability.

中文翻译：

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溃疡性结肠炎患者内镜疾病活动的新型计算机辅助诊断系统

背景和目标

在确定选择的治疗方法时，评估溃疡性结肠炎（UC）患者的内窥镜疾病活动很重要。但是，内镜医师需要经过一定的培训才能正确评估炎症活动，并且存在观察者之间的差异。因此，我们使用卷积神经网络（CNN）构建了计算机辅助诊断（CAD）系统，并使用来自UC患者的大量内窥镜图像数据集评估了其性能。

方法

基于GoogLeNet架构构建了基于CNN的CAD系统。使用来自总共841例UC患者的26,304例结肠镜检查图像对CNN进行了训练，这些图像标有解剖位置和Mayo内窥镜评分。通过计算114例UC患者的3981张图像的独立测试集，通过计算接收器工作特征曲线下的面积，对CNN识别正常黏膜（Mayo 0）和黏膜愈合状态（Mayo 0-1）的性能进行了评估（ AUROCs）。另外，评估了结肠右侧，结肠左侧和直肠中的AUROC。

结果

基于CNN的CAD系统显示出高水平的性能，其AUROC分别为0.86和0.98，分别可识别Mayo 0和0-1。识别Mayo 0（AUROC分别为0.92、0.83和0.83）时，CNN的直肠功能优于结肠的右侧和左侧。

结论

当用于识别UC患者的内窥镜炎症严重程度时，基于CNN的CAD系统的性能十分强大，突显了其在支持经验不足的内镜医师和减少观察者间变异性方面的有前途的作用。