Background

Gastric intestinal metaplasia (GIM) is a precancerous lesion of gastric cancer.

Currently, diagnosis of GIM is based on the experience of a physician, which is liable to interobserver variability. Thus, an intelligent diagnostic (ID) system, based on narrow-band and magnifying narrow-band images, was constructed to provide objective assistance in the diagnosis of GIM.

Method

We retrospectively collected 1880 endoscopic images (1048 GIM and 832 non-GIM) via biopsy from 336 patients confirmed histologically as GIM or non-GIM, from the Kiang Wu Hospital, Macau. We developed an ID system with these images using a modified convolutional neural network algorithm. A separate test dataset containing 477 pathologically confirmed images (242 GIM and 235 non-GIM) from 80 patients was used to test the performance of the ID system. Experienced endoscopists also examined the same test dataset, for comparison with the ID system. One of the challenges faced in this study was that it was difficult to obtain a large number of training images. Thus, data augmentation and transfer learning were applied together.

Results

The area under the receiver operating characteristic curve was 0.928 for the pre-patient analysis of the ID system, while the sensitivities, specificities, and accuracies of the ID system against those of the human experts were (91.9% vs. 86.5%, p-value = 1.000) (86.0% vs. 81.4%, p-value = 0.754), and (88.8% vs. 83.8%, p-value = 0.424), respectively. Even though the three indices of the ID system were slightly higher than those of the human experts, there were no significant differences.

Conclusions

In this pilot study, a novel ID system was developed to diagnose GIM. This system exhibits promising diagnostic performance. It is believed that the proposed system has the potential for clinical application in the future.

中文翻译：

---

基于卷积神经网络和有限数量的内窥镜图像的胃小肠化生的智能诊断

背景

胃肠上皮化生（GIM）是胃癌的癌前病变。

当前，GIM的诊断基于医师的经验，这易于观察者之间的差异。因此，构建了基于窄带和放大窄带图像的智能诊断（ID）系统，为GIM的诊断提供客观的帮助。

方法

我们通过活检从澳门江湖医院经组织学回顾性收集了1880例内镜图像（1048 GIM和832非GIM），经组织学确认为GIM或非GIM。我们使用改进的卷积神经网络算法开发了带有这些图像的ID系统。包含来自80位患者的477份经病理证实的图像（242张GIM和235张非GIM）的单独测试数据集用于测试ID系统的性能。经验丰富的内镜医师还检查了相同的测试数据集，以与ID系统进行比较。这项研究面临的挑战之一是很难获得大量的训练图像。因此，数据增强和转移学习被一起应用。

结果

对于ID系统的患者进行术前分析，接收器工作特性曲线下的面积为0.928，而ID系统相对于人类专家的敏感性，特异性和准确性分别为（91.9％对86.5％，p-值= 1.000）（分别为86.0％和81.4％，p值= 0.754）和（88.8％和83.8％，p值= 0.424）。即使ID系统的三个指标略高于人类专家的三个指标，也没有显着差异。

结论

在这项初步研究中，开发了一种新颖的ID系统来诊断GIM。该系统展现出有希望的诊断性能。据信，所提出的系统在将来具有临床应用的潜力。